FACE DETECTION AND RECOGNITION USING MOVING WINDOW ACCUMULATOR WITH VARIOUS DEEP LEARNING ARCHITECTURE
Nayak, Anil Kumar
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Recent advancement in the field of Computer Vision and Deep Learning is making object detection and recognition easier. Hence, growing research activities in the field of deep learning are enabling researchers to find new ideas in the area of face detection and recognition. Implementation of such systems has a number of challenges when it comes to the current approaches. In this paper, we have presented a system of Face Detection and Recognition with newly designed deep learning classification models like CNN, Inception and various state of art models like SVM and we also compared the result with FaceNet. Multiple approaches to the face recognition were presented, out of which training of deep neural network, SVM on embedding data are optimized for the recognition task by implementing a moving weighted accumulator at the post processing stage. The accumulator helps in storing of past recognized faces for decision making. For real-world testing, we have implemented a face detection and recognition graphical component, which has helped us in the testing of various deep learning models in real-world scenarios as well as to minimize the data collection efforts for incremental training of deep learning and classification models.