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Recent developments in chip technology have led to the integration of smart Artificial Intelligence (AI) capabilities in surveillance cameras such as vehicle and pedestrian detection, license plate recognition, and face detection (FD). FD cameras are being widely deployed in various public locations to identify wanted individuals. However, the large size of identity databases and the need for personal information security make it necessary to perform face recognition (FR) on servers rather than on cameras. On the other hand, more FD cameras and detected faces lead to extensive power consumption and computational power demand for real-time FR along with large storage devices to store face images. While FD cameras track detected faces and select the best quality face for FR, multiple faces belonging to the same identities might be sent to the servers due to track algorithms or reentering of identity to the scene. Therefore, we propose a method for finding similar faces belonging to the same identities and removing low-quality faces to keep only the highest-quality face in the server for storage and FR. We utilize facial embedding vectors, obtained from aligned faces using an FR model, and store them in a database along with face image information such as face capture time, camera IP, and face quality score. If a face has similar faces in the database, the highest-quality face is kept in the database and others are removed. As a result, our proposed method eliminates extra face images and keeps the highest-quality face images for high performance, efficient FR, and effective storage.
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