How do you train and update video analytics models for facial recognition at the edge?

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Facial recognition is a powerful application of video analytics that can enhance security, convenience, and personalization. However, it also poses challenges such as privacy, accuracy, and scalability. To address these issues, many facial recognition systems are moving from the cloud to the edge, where they can process video data locally on devices such as cameras, smartphones, or IoT sensors. But how do you train and update video analytics models for facial recognition at the edge? In this article, we will explore some key steps and considerations for doing so.

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Navdeep Thakur

???102x Linkedin Top voice?? | Digital marketing consultant | Data Enthusiast | PGDBA in Marketing | Helping Hand | 27K+
James Albert Silvoza

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Luis Alberto Nieto

SECURITY SOLUTIONS MANAGER | Security Systems Engineer | Master's Degree in Fire Protection | Certified Private…

1 Choose a suitable model architecture

The first step is to choose a model architecture that can perform facial recognition efficiently and accurately on the edge. This means balancing the trade-offs between speed, size, and accuracy. For example, you can use a lightweight model such as MobileNet or SqueezeNet that has fewer parameters and layers, but may sacrifice some accuracy. Or you can use a more complex model such as ResNet or VGG that has higher accuracy, but may be too slow or large for the edge. You can also use techniques such as pruning, quantization, or distillation to reduce the size and complexity of your model without losing too much performance.

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Navdeep Thakur

???102x Linkedin Top voice?? | Digital marketing consultant | Data Enthusiast | PGDBA in Marketing | Helping Hand | 27K+
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- Model Choice: Select lightweight architectures like MobileNet-V2/3 or SqueezeNet for efficient inference. - Model Optimization: Employ techniques like pruning, quantization, and distillation to reduce model size and complexity. - Privacy and Security: Prioritize data privacy and security measures. - Edge vs. Cloud: Evaluate the trade-offs between on-device processing and cloud-based solutions.

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James Albert Silvoza

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I train and update facial recognition models at the edge by processing local data and regularly integrating new datasets to enhance accuracy and adaptability, ensuring efficient and secure performance.

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Luis Alberto Nieto

SECURITY SOLUTIONS MANAGER | Security Systems Engineer | Master's Degree in Fire Protection | Certified Private Security Director | Consulting & Business Risk | Innovation and Technology |
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Para un sistema eficiente de reconocimiento facial en el borde, es crucial equilibrar velocidad, tama?o y precisión. Modelos ligeros como MobileNetV3, EfficientNet, y YOLO (You Only Look Once) destacan pra realizar una deteccion previa en tiempo real , sincronizandose con el reconocimiento facial en el borde, ofreciendo alta velocidad con buen rendimiento. Además técnicas como cuantización, pruning y destilación reducen el tama?o y complejidad del modelo sin perder precisión. El uso en mi experiencia de Aceleradores como TPUs/NPUs mejoran la velocidad. En modelos de seguridad basados en CCTV , es importante balancear la carga entre el borde y la nube, optimizando precisión y latencia en un escenario de analisis real.

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Navdeep Thakur

???102x Linkedin Top voice?? | Digital marketing consultant | Data Enthusiast | PGDBA in Marketing | Helping Hand | 27K+
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Facial Recognition: - - Enhanced Accuracy - Real-time Processing - Multimodal Fusion - Privacy and Ethics Blockchain: - - Data Security - Decentralized Identity Management - Transparency and Accountability - Integration with Smart Contracts

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Mervin Forero

Strategic Marketing Director | Expert in Security Integration Technologies | Specialized in CCTV, Access Control, Alarms, AI & Cloud Services | Brand Growth & Innovation Leader in Cannabis & Beverage Industries
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When tackling a facial recognition project for a remote facility with limited resources, we needed a model that balanced speed, size, and accuracy. MobileNet and SqueezeNet were lightweight and fast, but less accurate. ResNet and VGG offered high accuracy but were too large and slow for our needs. We used pruning and quantization to reduce model size and complexity without sacrificing too much performance. Distillation helped transfer knowledge from a larger model to a smaller one. After several iterations, we found a solution that balanced our needs perfectly, demonstrating the importance of choosing the right architecture for edge applications.

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2 Train your model with relevant data

The second step is to train your model with relevant data that reflects the real-world scenarios and conditions of your facial recognition system. This means collecting and labeling a diverse and representative dataset of faces that covers different angles, poses, expressions, lighting, occlusions, and variations. You also need to augment your data with transformations such as cropping, flipping, rotating, or adding noise to increase its robustness and generalization. You can use frameworks such as TensorFlow, PyTorch, or OpenCV to train your model with various optimization algorithms and loss functions.

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Luis Alberto Nieto

SECURITY SOLUTIONS MANAGER | Security Systems Engineer | Master's Degree in Fire Protection | Certified Private Security Director | Consulting & Business Risk | Innovation and Technology |
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Para entrenar un modelo, es vital recopilar datos diversos, cubriendo ángulos, expresiones, iluminación y oclusiones, y evitar sesgos demográficos. Bases de datos como LFW y VGGFace2 ayudan, pero normalmente es necesario crear datasets personalizados. Herramientas como Labelbox y SuperAnnotate facilitan la recolección y etiquetado. Marcos como TensorFlow (con TensorFlow Lite para el borde) y PyTorch es popular por su flexibilidad y facilidad de uso. Es excelente para entrenar modelos con aprendizaje profundo y ajustar hiperparámetros de manera rápida. Además, ofrece soporte en hardware especializado, como GPUs y TPUs., Para el etiquetado y segmentacion usar LabelImg y CVAT son útiles generando procesos de precisión.

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3 Deploy your model to the edge devices

The third step is to deploy your model to the edge devices where you want to perform facial recognition. This means converting your model to a format that is compatible with the hardware and software of your devices, such as TensorFlow Lite, ONNX, or Core ML. You also need to optimize your model for the specific constraints and capabilities of your devices, such as memory, battery, CPU, GPU, or NPU. You can use tools such as TensorFlow Model Optimization Toolkit, Intel OpenVINO, or NVIDIA TensorRT to optimize your model for the edge.

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Luis Alberto Nieto

SECURITY SOLUTIONS MANAGER | Security Systems Engineer | Master's Degree in Fire Protection | Certified Private Security Director | Consulting & Business Risk | Innovation and Technology |
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Para implementar un modelo de reconocimiento facial en dispositivos perimetrales, es clave convertirlo a formatos compatibles como TensorFlow Lite, ONNX o Core ML y optimizarlo según el hardware. Herramientas como TensorFlow Model Optimization Toolkit (cuantización y pruning), Intel OpenVINO, o NVIDIA TensorRT optimizan modelos para dispositivos como Jetson Nano o Jetson Orin, que con sus potentes GPUs ofrecen baja latencia y alta precisión. Para modelos más ligeros, dispositivos como Raspberry Pi 4 o Raspberry Pi 5, con mayores prestaciones, son una excelente opción para ejecutar modelos optimizados, combinando eficiencia energética y costo reducido en entornos de seguridad.

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4 Update your model with feedback

The fourth step is to update your model with feedback from the edge devices to improve its accuracy and adaptability. This means collecting and analyzing the results and performance of your model on the edge, such as accuracy, latency, power consumption, or errors. You also need to incorporate new data and labels from the edge devices to retrain or fine-tune your model with new or changing faces. You can use techniques such as federated learning, incremental learning, or active learning to update your model with feedback from the edge.

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5 Secure your model and data

The fifth step is to secure your model and data from potential threats and risks on the edge. This means encrypting and protecting your model and data from unauthorized access, tampering, or theft. You also need to comply with the regulations and ethical standards of your domain and region, such as GDPR, CCPA, or NIST. You can use methods such as homomorphic encryption, differential privacy, or watermarking to secure your model and data on the edge.

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6 Evaluate your model and system

The sixth step is to evaluate your model and system on the edge to measure its effectiveness and impact. This means testing and validating your model and system on various metrics and indicators, such as accuracy, precision, recall, F1-score, ROC curve, or AUC. You also need to assess your model and system on qualitative and subjective aspects, such as user satisfaction, trust, fairness, bias, or privacy. You can use frameworks such as Face Recognition Evaluation Platform, Face Recognition Vendor Test, or Face Recognition Grand Challenge to evaluate your model and system on the edge.

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How do you train and update video analytics models for facial recognition at the edge?

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1 Choose a suitable model architecture

2 Train your model with relevant data

3 Deploy your model to the edge devices

4 Update your model with feedback

5 Secure your model and data

6 Evaluate your model and system

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