Paravision Plugin

Description

Paravision is a specialized integration plugin for Paravision face recognition technology. It provides facial recognition and biometric identification capabilities through integration with Paravision's proprietary algorithms and hardware acceleration platforms.

This plugin enables enterprise-grade facial recognition and biometric identification through integration with Paravision's advanced computer vision algorithms, supporting both Windows and specialized hardware acceleration platforms for high-performance real-time face recognition applications.

Key Features

• Advanced Face Recognition: State-of-the-art facial recognition using Paravision's proprietary algorithms
• Biometric Processing: Comprehensive biometric data processing, extraction, and analysis
• Hardware Acceleration: Support for specialized hardware acceleration (Hailo AI processors, GPU acceleration)
• Real-time Processing: High-performance real-time face detection and recognition processing
• Identity Management: Integration with identity databases and management systems
• Multi-Platform Support: Optimized implementations for Windows and embedded platforms
• Security Features: Enhanced security and privacy protection mechanisms
• Quality Assessment: Advanced face quality assessment and filtering
• Embedding Generation: High-quality face embeddings for similarity matching
• Asynchronous Processing: Async inference support for high-throughput applications
• Scalable Architecture: Support for multi-threaded and distributed processing
• Performance Monitoring: Built-in performance monitoring and optimization

Requirements

Hardware Requirements

Windows Platform

• CPU: Multi-core x64 processor (Intel/AMD)
• Memory: Minimum 8GB RAM (16GB+ recommended for high-throughput)
• GPU: NVIDIA GPU with CUDA support (recommended for acceleration)
• Storage: SSD storage for model and database access

Hailo Platform

• Hailo AI Processor: Hailo-8 or Hailo-15 AI acceleration chips
• Memory: Sufficient system memory for model loading and processing
• Host Processor: ARM or x86 host processor for system management

Software Dependencies

Core Dependencies

• Paravision SDK: Proprietary Paravision face recognition libraries and runtime
• CVEDIA-RT Core: Base plugin infrastructure and interfaces
• Threading Libraries: Multi-threading support for concurrent processing
• Mathematical Libraries: Linear algebra and statistical computation libraries

Platform-Specific Dependencies

Windows Platform

• Visual C++ Redistributable: Microsoft Visual C++ runtime libraries
• CUDA Runtime: NVIDIA CUDA runtime (if GPU acceleration used)
• OpenCV: Computer vision library for image processing
• DirectX: DirectX runtime for GPU operations

Hailo Platform

• HailoRT: Hailo runtime library for AI processor communication
• Hailo SDK: Hailo development kit and drivers
• ARM Libraries: ARM-specific libraries for embedded deployment

Licensing Requirements

• Paravision License: Valid Paravision SDK license for face recognition algorithms
• Hardware License: Appropriate hardware acceleration licenses (if applicable)
• CVEDIA-RT License: Valid CVEDIA-RT commercial license

Configuration

Basic Configuration

{
  "paravision": {
    "model_path": "/models/paravision_face_model.bin",
    "detection_threshold": 0.7,
    "recognition_threshold": 0.8,
    "max_faces": 10,
    "enable_quality_filter": true,
    "platform": "auto"
  }
}

Advanced Configuration

{
  "paravision": {
    "model_path": "/models/paravision_face_model.bin",
    "detection_threshold": 0.75,
    "recognition_threshold": 0.85,
    "quality_threshold": 0.6,
    "max_faces": 20,
    "enable_quality_filter": true,
    "enable_pose_filter": true,
    "platform": "hailo",
    "hardware_acceleration": {
      "enabled": true,
      "device_type": "hailo15",
      "batch_size": 4,
      "async_processing": true
    },
    "processing_options": {
      "input_width": 224,
      "input_height": 224,
      "color_format": "RGB",
      "normalization": true,
      "face_alignment": true
    },
    "performance": {
      "pool_size": 4,
      "async_results_duration": 1000,
      "enable_profiling": true,
      "memory_optimization": true
    },
    "quality_assessment": {
      "min_face_size": 80,
      "max_face_size": 400,
      "blur_threshold": 0.5,
      "brightness_range": [30, 200],
      "pose_angle_limit": 30
    }
  }
}

Configuration Schema

Parameter	Type	Default	Description
model_path	string	required	Path to Paravision face recognition model
detection_threshold	float	0.7	Face detection confidence threshold (0.0-1.0)
recognition_threshold	float	0.8	Face recognition similarity threshold (0.0-1.0)
quality_threshold	float	0.5	Face quality assessment threshold (0.0-1.0)
max_faces	int	10	Maximum number of faces to process per frame
enable_quality_filter	bool	true	Enable face quality filtering
enable_pose_filter	bool	false	Enable face pose filtering
platform	string	"auto"	Target platform ("auto", "windows", "hailo")
batch_size	int	1	Processing batch size for hardware acceleration
async_processing	bool	false	Enable asynchronous processing
input_width	int	224	Model input width in pixels
input_height	int	224	Model input height in pixels
color_format	string	"RGB"	Input color format ("RGB", "BGR")
normalization	bool	true	Enable input normalization
face_alignment	bool	true	Enable face alignment preprocessing
pool_size	int	1	Inference engine pool size
async_results_duration	int	1000	Async results collection duration (ms)
enable_profiling	bool	false	Enable performance profiling
memory_optimization	bool	true	Enable memory usage optimization
min_face_size	int	50	Minimum face size in pixels
max_face_size	int	500	Maximum face size in pixels
blur_threshold	float	0.5	Blur detection threshold
pose_angle_limit	int	45	Maximum pose angle in degrees

API Reference

C++ API (ParavisionManaged)

Core Inference Methods

class ParavisionManaged : public iface::InferenceEngine {
public:
    // Model management
    expected<void> loadModelFromConfig() override;
    expected<void> loadModel(std::string const& path) override;
    expected<void> loadModel(std::string const& path, CValue& handlerConfig) override;

    // Inference operations
    expected<cvec> runInference(cvec const& jobs) override;
    expected<void> runInferenceAsync(cvec const& jobs, int ttl = 60) override;
    expected<cvec> getAsyncInferenceResults() override;

    // Configuration and capabilities
    expected<pCValue> getCapabilities() override;
    expected<pCValue> getModelConfig() override;
    void setBackendConfig(pCValue conf) override;

    // Model information
    ssize_t inputBatchSize() override;
    ssize_t inputWidth() override;
    ssize_t inputHeight() override;
    ssize_t inputChannels() override;
    std::vector<int> inputShape() override;
    std::vector<int> outputShape() override;
};

Face Recognition Data Structures

// Face bounding box (platform-specific)
#ifdef WIN32
    using FaceBBox = Paravision::Recognition::BoundingBox;
#elif HAILO_15_GREENBASE_BUILD
    using FaceBBox = Paravision::BoundingBox;
#endif

// Face embedding result
struct ParavisionInferenceResult {
    std::shared_ptr<Paravision::Recognition::Embedding> embedding;
    float quality;
    float confidence;
    FaceBBox boundingBox;
};

// Inference job data
struct InferenceJobData {
    std::shared_ptr<FaceBBox> bbox;
    cbuffer frame;
    std::string job_id;
    double timestamp;
};

Platform-Specific Components

#if HAILO_15_GREENBASE_BUILD
class ParavisionManaged {
private:
    std::unique_ptr<Paravision::CoreComponents> coreComponents_;
    std::unique_ptr<Paravision::Detection::SDK> detectionSdk_;
    std::unique_ptr<Paravision::Recognition::SDK> recognitionSdk_;
};
#else
class ParavisionManaged {
private:
    std::unique_ptr<Paravision::Recognition::SDK> recognitionSdk_;
};
#endif

Async Processing

class ParavisionManaged {
public:
    // Async inference management
    expected<void> setAsyncResultsCollectionDuration(int durationMs) override;
    expected<void> setPoolSize(int poolSize) override;

private:
    std::unique_ptr<AsyncInferenceWorker> asyncWorker_;
    std::atomic<bool> modelLoaded_ = false;
    int inferenceCount_ = 0;
};

Lua API

Model Loading and Configuration

-- Create Paravision inference engine
local paravision = api.factory.inference.create(instance, "paravision_engine")

-- Configure for face recognition
paravision:configure({
    engine = "paravision",
    model_path = "/models/paravision_face.bin",
    detection_threshold = 0.75,
    recognition_threshold = 0.85,
    platform = "auto",
    hardware_acceleration = true
})

-- Load model
local success = paravision:loadModel()
if success then
    print("Paravision model loaded successfully")

    -- Get model information
    local modelConfig = paravision:getModelConfig()
    print("Input size:", modelConfig.input_width .. "x" .. modelConfig.input_height)
    print("Batch size:", paravision:inputBatchSize())
else
    print("Failed to load Paravision model")
end

Face Recognition Processing

-- Process face recognition
function processFaceRecognition(frame, faceDetections)
    local recognitionJobs = {}

    -- Create recognition jobs from face detections
    for _, detection in ipairs(faceDetections) do
        local job = {
            frame = frame,
            bbox = {
                x = detection.x,
                y = detection.y,
                w = detection.w,
                h = detection.h
            },
            job_id = "face_" .. detection.id,
            timestamp = api.system.getCurrentTime()
        }
        table.insert(recognitionJobs, job)
    end

    -- Run face recognition inference
    local results = paravision:runInference(recognitionJobs)

    if results then
        for _, result in ipairs(results) do
            processRecognitionResult(result)
        end
    end
end

-- Process recognition result
function processRecognitionResult(result)
    print("Face Recognition Result:")
    print("  Job ID:", result.job_id)
    print("  Quality:", result.quality)
    print("  Confidence:", result.confidence)

    if result.embedding then
        print("  Embedding size:", #result.embedding)
        -- Compare with database embeddings
        local match = findBestMatch(result.embedding, faceDatabase)
        if match and match.similarity > 0.85 then
            print("  Match found:", match.identity, "(" .. match.similarity .. ")")
        else
            print("  No match found")
        end
    end
end

Async Processing

-- Configure async processing
paravision:setPoolSize(4)
paravision:setAsyncResultsCollectionDuration(1000)

-- Submit async jobs
function submitAsyncRecognition(faceJobs)
    local success = paravision:runInferenceAsync(faceJobs, 60)  -- 60 second TTL
    if success then
        print("Submitted", #faceJobs, "face recognition jobs")
    end
end

-- Collect async results
function collectAsyncResults()
    local results = paravision:getAsyncInferenceResults()
    if results and #results > 0 then
        print("Retrieved", #results, "async results")
        for _, result in ipairs(results) do
            processRecognitionResult(result)
        end
    end
end

-- Process with async pattern - implement in main loop
-- Check every N frames or use a frame counter
-- Example: if frame_count % 15 == 0 then collectAsyncResults() end

Examples

Basic Face Recognition System

#include "paravision_managed.h"

// Basic face recognition implementation
class FaceRecognitionSystem {
public:
    void initialize() {
        // Create Paravision inference engine
        paravision_ = std::unique_ptr<ParavisionManaged>(
            static_cast<ParavisionManaged*>(
                ParavisionManaged::create("face_recognition").release()
            )
        );

        // Configure for face recognition
        auto config = CValue::create();
        config->set("model_path", "/models/paravision_face.bin");
        config->set("detection_threshold", 0.75);
        config->set("recognition_threshold", 0.85);
        config->set("max_faces", 10);
        config->set("enable_quality_filter", true);

        paravision_->setBackendConfig(config);

        // Load model
        auto loadResult = paravision_->loadModelFromConfig();
        if (!loadResult) {
            LOGE << "Failed to load Paravision model: " << loadResult.error().message();
            return;
        }

        // Get model information
        auto capabilities = paravision_->getCapabilities();
        if (capabilities) {
            LOGI << "Paravision model capabilities:";
            LOGI << "  Input size: " << paravision_->inputWidth() 
                 << "x" << paravision_->inputHeight();
            LOGI << "  Batch size: " << paravision_->inputBatchSize();
        }

        LOGI << "Face recognition system initialized";
    }

    std::vector<RecognitionResult> recognizeFaces(const cbuffer& frame, 
                                                 const std::vector<FaceDetection>& faces) {
        std::vector<RecognitionResult> results;

        // Prepare inference jobs
        cvec jobs;
        for (const auto& face : faces) {
            auto job = createRecognitionJob(frame, face);
            if (job) {
                jobs.push_back(job);
            }
        }

        if (jobs.empty()) {
            return results;
        }

        // Run inference
        auto inferenceResults = paravision_->runInference(jobs);
        if (!inferenceResults) {
            LOGE << "Face recognition inference failed: " 
                 << inferenceResults.error().message();
            return results;
        }

        // Process results
        for (const auto& result : inferenceResults.value()) {
            auto recognition = processInferenceResult(result);
            if (recognition.has_value()) {
                results.push_back(recognition.value());
            }
        }

        LOGI << "Recognized " << results.size() << " faces from " 
             << faces.size() << " detections";

        return results;
    }

private:
    std::unique_ptr<ParavisionManaged> paravision_;

    struct FaceDetection {
        int x, y, w, h;
        float confidence;
        std::string id;
    };

    struct RecognitionResult {
        std::string face_id;
        std::vector<float> embedding;
        float quality;
        float confidence;
        FaceDetection detection;
    };

    pCValue createRecognitionJob(const cbuffer& frame, const FaceDetection& face) {
        auto job = CValue::create();

        // Set frame data
        job->set("frame", frame);

        // Set bounding box
        auto bbox = CValue::create();
        bbox->set("x", face.x);
        bbox->set("y", face.y);
        bbox->set("w", face.w);
        bbox->set("h", face.h);
        job->set("bbox", bbox);

        // Set metadata
        job->set("job_id", face.id);
        job->set("timestamp", getCurrentTimestamp());

        return job;
    }

    std::optional<RecognitionResult> processInferenceResult(pCValue result) {
        if (!result || result->isNull()) {
            return std::nullopt;
        }

        RecognitionResult recognition;

        // Extract basic information
        recognition.face_id = result->get("job_id").getString();
        recognition.quality = result->get("quality").getFloat();
        recognition.confidence = result->get("confidence").getFloat();

        // Extract embedding
        auto embeddingValue = result->get("embedding");
        if (embeddingValue.isArray()) {
            for (const auto& val : embeddingValue.getVector()) {
                recognition.embedding.push_back(val->getFloat());
            }
        }

        // Extract bounding box
        auto bboxValue = result->get("bbox");
        if (!bboxValue.isNull()) {
            recognition.detection.x = bboxValue->get("x").getInt();
            recognition.detection.y = bboxValue->get("y").getInt();
            recognition.detection.w = bboxValue->get("w").getInt();
            recognition.detection.h = bboxValue->get("h").getInt();
        }

        return recognition;
    }

    double getCurrentTimestamp() {
        return std::chrono::duration_cast<std::chrono::milliseconds>(
            std::chrono::steady_clock::now().time_since_epoch()).count() / 1000.0;
    }
};

High-Performance Async Face Recognition

// High-performance async face recognition system
class AsyncFaceRecognitionSystem {
public:
    void initialize() {
        // Initialize Paravision with async support
        paravision_ = std::unique_ptr<ParavisionManaged>(
            static_cast<ParavisionManaged*>(
                ParavisionManaged::create("async_face_recognition").release()
            )
        );

        // Configure for high-performance processing
        auto config = CValue::create();
        config->set("model_path", "/models/paravision_fast.bin");
        config->set("detection_threshold", 0.7);
        config->set("recognition_threshold", 0.8);
        config->set("batch_size", 8);
        config->set("async_processing", true);
        config->set("hardware_acceleration", true);

        paravision_->setBackendConfig(config);
        paravision_->loadModelFromConfig();

        // Configure async processing
        paravision_->setPoolSize(8);  // 8 concurrent workers
        paravision_->setAsyncResultsCollectionDuration(500);  // 500ms collection

        // Start result collection thread
        startResultCollection();

        LOGI << "Async face recognition system initialized";
    }

    void submitFaceRecognitionBatch(const std::vector<FaceJob>& faceJobs) {
        // Convert to CValue jobs
        cvec jobs;
        for (const auto& faceJob : faceJobs) {
            auto job = createJobFromFaceJob(faceJob);
            jobs.push_back(job);
        }

        // Submit for async processing
        auto result = paravision_->runInferenceAsync(jobs, 60);  // 60 second TTL
        if (result) {
            LOGI << "Submitted " << jobs.size() << " face recognition jobs";
            totalJobsSubmitted_ += jobs.size();
        } else {
            LOGE << "Failed to submit async jobs: " << result.error().message();
        }
    }

private:
    std::unique_ptr<ParavisionManaged> paravision_;
    std::thread resultCollectionThread_;
    std::atomic<bool> running_{true};
    std::atomic<int> totalJobsSubmitted_{0};
    std::atomic<int> totalResultsProcessed_{0};

    struct FaceJob {
        std::string job_id;
        cbuffer frame;
        FaceDetection detection;
        double submission_time;
    };

    void startResultCollection() {
        resultCollectionThread_ = std::thread([this]() {
            collectAsyncResults();
        });
    }

    void collectAsyncResults() {
        while (running_) {
            auto results = paravision_->getAsyncInferenceResults();
            if (results && !results.value().empty()) {
                processAsyncResults(results.value());
            }

            std::this_thread::sleep_for(std::chrono::milliseconds(100));
        }
    }

    void processAsyncResults(const cvec& results) {
        for (const auto& result : results) {
            processRecognitionResult(result);
        }

        totalResultsProcessed_ += results.size();

        // Log performance metrics
        if (totalResultsProcessed_ % 100 == 0) {
            double processingRate = static_cast<double>(totalResultsProcessed_) / 
                                  (getCurrentTimestamp() - startTime_);
            LOGI << "Face recognition rate: " << processingRate << " faces/sec";
        }
    }

    double startTime_ = getCurrentTimestamp();
};

Complete Identity Management Integration

-- Complete face recognition and identity management system
local paravision = api.factory.inference.create(instance, "identity_system")
local database = api.factory.database.create(instance, "identity_db")
local detector = api.factory.inference.create(instance, "face_detector")

-- Identity database management
local identityDatabase = {
    known_faces = {},
    recognition_log = {},
    active_sessions = {}
}

-- Initialize complete system
function initializeIdentitySystem()
    print("Initializing Paravision identity management system")

    -- Configure face detection
    detector:configure({
        engine = "tensorrt",
        model_path = "/models/face_detection.trt",
        detection_threshold = 0.7,
        nms_threshold = 0.4
    })

    -- Configure Paravision face recognition
    paravision:configure({
        engine = "paravision",
        model_path = "/models/paravision_identity.bin",
        detection_threshold = 0.75,
        recognition_threshold = 0.85,
        quality_threshold = 0.6,
        platform = "hailo",
        hardware_acceleration = true,
        async_processing = true,
        pool_size = 6
    })

    -- Load models
    detector:loadModel()
    paravision:loadModel()

    -- Initialize database
    database:connect("sqlite:///data/identity.db")
    loadKnownIdentities()

    -- Start async result collection
    startAsyncProcessing()

    print("Identity system initialized with " .. #identityDatabase.known_faces .. " known identities")
end

-- Load known identities from database
function loadKnownIdentities()
    local query = "SELECT id, name, embedding, metadata FROM identities WHERE active = 1"
    local results = database:query(query)

    for _, row in ipairs(results) do
        identityDatabase.known_faces[row.id] = {
            id = row.id,
            name = row.name,
            embedding = api.json.decode(row.embedding),
            metadata = api.json.decode(row.metadata or "{}")
        }
    end
end

-- Process frame for identity recognition
function processFrameForIdentity(frame)
    local startTime = api.system.getCurrentTime()

    -- Detect faces
    local detections = detector:runInference({frame})
    if not detections or #detections == 0 then
        return {}
    end

    -- Filter quality faces
    local qualityFaces = filterFacesByQuality(detections[1].detections)

    if #qualityFaces == 0 then
        return {}
    end

    -- Submit for async recognition
    local recognitionJobs = {}
    for _, face in ipairs(qualityFaces) do
        local job = {
            frame = frame,
            bbox = face.bbox,
            job_id = "face_" .. face.id .. "_" .. startTime,
            timestamp = startTime,
            detection_confidence = face.confidence
        }
        table.insert(recognitionJobs, job)
    end

    -- Submit async jobs
    paravision:runInferenceAsync(recognitionJobs, 60)

    -- Debug: Submitted N faces for recognition

    return qualityFaces
end

-- Filter faces by quality criteria
function filterFacesByQuality(detections)
    local qualityFaces = {}

    for _, detection in ipairs(detections) do
        local face = detection

        -- Size filter
        local faceSize = math.max(face.bbox.w, face.bbox.h)
        if faceSize < 80 or faceSize > 400 then
            goto continue
        end

        -- Confidence filter
        if face.confidence < 0.7 then
            goto continue
        end

        -- Aspect ratio filter
        local aspectRatio = face.bbox.w / face.bbox.h
        if aspectRatio < 0.7 or aspectRatio > 1.4 then
            goto continue
        end

        table.insert(qualityFaces, face)

        ::continue::
    end

    return qualityFaces
end

-- Start async result processing
function startAsyncProcessing()
    -- Implement periodic checking in your main processing loop
    -- Check every few frames for async results
    -- Example implementation:
    local function checkAsyncResults()
        local results = paravision:getAsyncInferenceResults()
        if results and #results > 0 then
            for _, result in ipairs(results) do
                processRecognitionResult(result)
            end
        end
    end
end

-- Process individual recognition result
function processRecognitionResult(result)
    if not result.embedding or result.quality < 0.6 then
        -- Debug: Low quality recognition result, skipping
        return
    end

    -- Find best match in database
    local bestMatch = findBestIdentityMatch(result.embedding)

    local identityResult = {
        job_id = result.job_id,
        timestamp = result.timestamp,
        quality = result.quality,
        confidence = result.confidence,
        bbox = result.bbox,
        embedding = result.embedding
    }

    if bestMatch and bestMatch.similarity > 0.85 then
        -- Known identity recognized
        identityResult.identity_id = bestMatch.identity.id
        identityResult.identity_name = bestMatch.identity.name
        identityResult.similarity = bestMatch.similarity
        identityResult.match_type = "known"

        print("Identity recognized: " .. bestMatch.identity.name .. " (" .. bestMatch.similarity .. " similarity, " .. result.quality .. " quality)")

        -- Update session tracking
        updateActiveSession(bestMatch.identity.id, identityResult)

    else
        -- Unknown identity
        identityResult.identity_id = "unknown"
        identityResult.identity_name = "Unknown"
        identityResult.similarity = bestMatch and bestMatch.similarity or 0.0
        identityResult.match_type = "unknown"

        -- Potentially add to unknown faces database
        if result.quality > 0.8 then
            addUnknownFace(identityResult)
        end
    end

    -- Log recognition event
    logRecognitionEvent(identityResult)

    -- Trigger alerts if needed
    checkForAlerts(identityResult)
end

-- Find best matching identity
function findBestIdentityMatch(queryEmbedding)
    local bestMatch = nil
    local bestSimilarity = 0.0

    for _, identity in pairs(identityDatabase.known_faces) do
        local similarity = calculateCosineSimilarity(queryEmbedding, identity.embedding)

        if similarity > bestSimilarity then
            bestSimilarity = similarity
            bestMatch = {
                identity = identity,
                similarity = similarity
            }
        end
    end

    return bestMatch
end

-- Calculate cosine similarity between embeddings
function calculateCosineSimilarity(embedding1, embedding2)
    if #embedding1 ~= #embedding2 then
        return 0.0
    end

    local dotProduct = 0.0
    local norm1 = 0.0
    local norm2 = 0.0

    for i = 1, #embedding1 do
        dotProduct = dotProduct + embedding1[i] * embedding2[i]
        norm1 = norm1 + embedding1[i] * embedding1[i]
        norm2 = norm2 + embedding2[i] * embedding2[i]
    end

    local magnitude = math.sqrt(norm1) * math.sqrt(norm2)
    return magnitude > 0 and dotProduct / magnitude or 0.0
end

-- Update active session tracking
function updateActiveSession(identityId, recognitionResult)
    local currentTime = api.system.getCurrentTime()

    if not identityDatabase.active_sessions[identityId] then
        -- New session
        identityDatabase.active_sessions[identityId] = {
            identity_id = identityId,
            start_time = currentTime,
            last_seen = currentTime,
            recognition_count = 1,
            avg_quality = recognitionResult.quality,
            avg_similarity = recognitionResult.similarity
        }
    else
        -- Update existing session
        local session = identityDatabase.active_sessions[identityId]
        session.last_seen = currentTime
        session.recognition_count = session.recognition_count + 1

        -- Update averages
        session.avg_quality = (session.avg_quality + recognitionResult.quality) / 2
        session.avg_similarity = (session.avg_similarity + recognitionResult.similarity) / 2
    end
end

-- System performance monitoring
function monitorSystemPerformance()
    -- Implement periodic monitoring in your main loop
    -- Use frame counters or time checks with os.time()
    local function checkPerformance()
        local stats = {
            known_identities = #identityDatabase.known_faces,
            active_sessions = 0,
            recognition_rate = 0
        }

        -- Count active sessions (seen in last 60 seconds)
        local currentTime = api.system.getCurrentTime()
        for _, session in pairs(identityDatabase.active_sessions) do
            if currentTime - session.last_seen < 60 then
                stats.active_sessions = stats.active_sessions + 1
            end
        end

        print("Identity System Stats:")
        -- Print stats details
    end)
end

-- Initialize the complete system
initializeIdentitySystem()
monitorSystemPerformance()

print("Paravision identity management system ready")

Best Practices

Model Optimization

• Platform-Specific Models: Use models optimized for target platform (Windows/Hailo)
• Quality Assessment: Implement comprehensive face quality filtering
• Batch Processing: Use appropriate batch sizes for hardware acceleration
• Memory Management: Optimize memory usage for embedded deployments

Performance Guidelines

• Async Processing: Use async inference for high-throughput applications
• Hardware Acceleration: Leverage available hardware acceleration (GPU, Hailo)
• Pool Sizing: Configure appropriate pool sizes based on concurrent load
• Quality Filtering: Filter low-quality faces before recognition

Integration Strategies

• Face Detection: Combine with high-quality face detection for optimal results
• Database Integration: Implement efficient identity database management
• Session Tracking: Track identity sessions for analytics and monitoring
• Alert Systems: Integrate with alert systems for security applications

Troubleshooting

Common Issues

Model Loading Failures

// Check model path and permissions
if (!std::filesystem::exists(modelPath)) {
    LOGE << "Paravision model file not found: " << modelPath;
    return;
}

// Verify Paravision SDK installation
if (!isParavisionSDKAvailable()) {
    LOGE << "Paravision SDK not properly installed";
    return;
}

Hardware Acceleration Issues

• Hailo Platform: Verify HailoRT installation and device accessibility
• GPU Acceleration: Check CUDA installation and GPU compatibility
• Driver Issues: Ensure proper hardware drivers are installed

Recognition Accuracy Problems

• Quality Thresholds: Adjust quality and confidence thresholds
• Face Alignment: Ensure proper face alignment preprocessing
• Lighting Conditions: Address poor lighting conditions in input images
• Database Quality: Ensure high-quality reference images in database

Debugging Tools

// Paravision system diagnostics
void diagnoseParavisionSystem() {
    // Check SDK availability
    if (!checkParavisionSDK()) {
        LOGE << "Paravision SDK not available";
    }

    // Check platform capabilities
    auto caps = paravision_->getCapabilities();
    if (caps) {
        LOGI << "Paravision Capabilities:";
        for (const auto& [key, value] : caps->getMap()) {
            LOGI << "  " << key << ": " << value.toString();
        }
    }

    // Monitor performance
    auto startTime = std::chrono::high_resolution_clock::now();
    // ... run test inference ...
    auto endTime = std::chrono::high_resolution_clock::now();
    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(endTime - startTime);

    LOGI << "Test inference time: " << duration.count() << "ms";
}

Integration Examples

Enterprise Access Control System

// Complete access control system with Paravision
class AccessControlSystem {
public:
    void initialize() {
        // Initialize face recognition
        initializeParavision();

        // Load authorized personnel database
        loadAuthorizedPersonnel();

        // Setup access control logic
        initializeAccessControl();

        // Start monitoring
        startSystemMonitoring();
    }

    void processAccessRequest(const VideoFrame& frame, 
                             const std::vector<FaceDetection>& faces) {
        for (const auto& face : faces) {
            auto recognition = recognizeFace(frame, face);

            if (recognition.has_value()) {
                processAccessDecision(recognition.value());
            }
        }
    }

private:
    void processAccessDecision(const RecognitionResult& result) {
        if (result.confidence > 0.85 && 
            isAuthorizedPersonnel(result.identity_id)) {

            grantAccess(result.identity_id);
            logAccessEvent(result, "GRANTED");

        } else {
            denyAccess();
            logAccessEvent(result, "DENIED");
        }
    }
};

See Also

• Inference Plugins Overview - AI inference integration and engines
• Platform Plugins Overview - All platform-specific plugins
• Plugin Overview - Complete plugin ecosystem
• JetsonUtils Plugin - NVIDIA Jetson platform integration
• Identity Management Guide - Identity and biometric system integration