Real-time face recognition on mobile devices has become essential in many areas. This technology holds great potential across various fields, including security and authentication, personalized user services, and social media applications. In this blog, we will walk through the step-by-step process of implementing real-time face recognition using mobile AI.

1. Fundamentals of Real-Time Face Recognition

Face recognition technology fundamentally involves detecting faces, extracting features from the detected face, and comparing them with a database. This process generally consists of three stages: face detection, feature extraction, and face recognition.

• Face Detection: This is the stage of detecting where faces are located in an image. Lightweight models such as MobileNet or Mediapipe can be used for efficient detection even on mobile devices.

  • You can use OpenCV to capture camera streams and Mediapipe for real-time face detection. First, initialize Mediapipe's face detection module, and then continuously process the frames coming from the camera to detect the position of faces. The coordinates of the detected face can be used to extract the face region.

• Feature Extraction: In this stage, features are extracted from the detected face and represented as vectors that capture the unique patterns of the face. A pre-trained Convolutional Neural Network (CNN) is typically used for this purpose.

  • The detected face image is fed into a pre-trained CNN model to extract unique facial features. TensorFlow Lite can be used to run the CNN model on a mobile device, and the extracted features are usually represented as a 128-dimensional vector, which can later be used for face comparison or recognition.

• Face Recognition: This stage involves comparing the extracted feature vectors to recognize a specific individual. This process generally uses methods like Cosine Similarity.

  • Calculate the similarity between feature vectors to recognize faces. For instance, store a user's vector in the database and compare a new face feature vector with the stored ones to find the most similar vector. Cosine Similarity can be used to calculate the similarity between the two vectors, enhancing recognition accuracy.

2. Choosing an AI Model Optimized for Mobile Environment

To implement real-time face recognition on mobile devices, it is crucial to use lightweight models. Models like MobileNet are designed specifically for mobile environments and offer high accuracy and fast inference speed. Additionally, Google Mediapipe can be used for the face detection stage, allowing for easy application in mobile environments.

• Lightweight models like MobileNet are designed considering the limited computational power of mobile devices. When selecting a model, balance accuracy and speed, and apply optimization techniques like quantization to reduce model size and improve inference speed. Using TensorFlow Lite's quantization tool, a trained model can be converted to 8-bit precision, reducing memory usage.

3. Advantages of On-device AI

For real-time face recognition, running the AI model directly on the mobile device rather than sending data to a cloud server has several advantages. This approach ensures better privacy, reduces network latency, and guarantees fast response times. Using frameworks like ZETIC.MLange allows easy conversion of existing AI models to On-device AI, making them usable on various mobile devices.

• Use ZETIC.MLange to optimize existing AI models for mobile devices. This way, the model can be run locally without communicating with cloud servers, ensuring enhanced data security as no personal data is transmitted externally.

4. Implementation Process

1. Model Preparation: Select a pre-trained face detection and recognition model. MobileNet or ResNet-based models are recommended.

   • Download pre-trained models from platforms like TensorFlow Hub or Hugging Face. It is crucial to select a lightweight model suitable for mobile environments.

2. Model Optimization: Optimize the model to run on mobile devices by applying techniques like quantization or pruning.

   • Use TensorFlow Lite Converter to convert the model to TFLite format and reduce the model size through quantization. Pruning can be applied to remove unnecessary neurons, further maximizing model lightweightness.

3. Mobile App Integration: Integrate the optimized model into a mobile application. TensorFlow Lite or ONNX Runtime can be used for this purpose.

   • Use Android Studio or Xcode to add the TensorFlow Lite library to the mobile application project. Then, include the optimized model file in the app's resource directory and write the code to load and run inference using Java/Kotlin or Swift.

4. Real-Time Processing: Process video streams from the mobile camera in real-time to detect and recognize faces. Use libraries like OpenCV for handling camera input.

   • Capture frames continuously from the camera using OpenCV, and feed each frame into the TensorFlow Lite model to detect and recognize faces. To enhance performance, use a separate thread to handle both camera streaming and model inference in parallel.

5. Performance Optimization Tips

• Utilizing NPU: Modern mobile devices are equipped with Neural Processing Units (NPUs), which can significantly increase the inference speed of AI models. Utilizing NPUs can improve the response time of face recognition.

  • When running the model on a device with an NPU, set TensorFlow Lite's delegate to automatically use the NPU. This will enable the model to run on the NPU instead of the GPU or CPU, maximizing performance.

• Model Lightweighting: It is crucial to reduce the model size through quantization to decrease memory usage on mobile devices.

  • In addition to quantization, techniques like pruning and knowledge distillation can be used to reduce model size and maximize efficiency without sacrificing performance.

• Thermal Management: Running real-time face recognition for extended periods can cause the device to overheat. Apply optimized inference frequencies and power management techniques to address this issue.

  • Adjust the inference cycle to reduce computational load and configure face recognition to be performed only when necessary. Additionally, use the device's power management API to control the processor's clock speed, thereby reducing heat generation.

6. Explore Related Blog Posts

If you want to learn more about implementing face recognition as an On-device AI, check out the related blog posts below:

• How to Implement Face Detection On-device: A guide on converting face detection models to On-device AI.

• How to Implement Face Landmark On-device: Explains how to convert face landmark models to On-device AI to accurately detect facial features.

• How to Implement Face Emotion On-device: Introduces the implementation of face emotion recognition technology for real-time analysis on mobile devices.

Conclusion

Real-time face recognition technology using mobile AI is now becoming more accessible to the general public. By leveraging ZETIC.ai's On-device AI solutions, existing AI models can be easily implemented on mobile devices, making them applicable to a wide range of user-tailored applications. With the advancement of mobile AI, we can expect more innovative applications to emerge in the future.