MULTI-CNN MODEL TO EVALUATE THE PERFORMANCE OF FACE DETECTION AND RECOGNITION WITH FACIAL FEATURE DETECTION AND RECOGNITION

Face Recognition is one of the most advanced and drastically growing research areas because it helps identify people globally in various ethical and unethical applications. Face recognition needs face detection that can be compared with a list of available faces to predict the correct person. Face detection has become popular, easy, and fast since it follows the Viola-Jones FD method. Face comparison is obtained by comparing the internal and external information from the face images, like different features, face structure, key points, and patch-by-patch comparison. Earlier face recognition methods used separate algorithms for feature extraction from the face images, like color, shape, texture, histogram, and local and global binary pattern, to compare pairs of images where they provide more complexity regarding computation, cost, and time. After the evolution of artificial intelligence models, recent research has focused on using machine and deep learning algorithms for face detection and recognition. However, the accuracy of face recognition models needs to be improved under various conditions. Thus, this paper used a two-stage face comparison model to enhance face recognition efficiency. A consequence of three CNN models called CNN-1, CNN-2, and CNN-3 are used to detect the faces, detect the facial features, and recognize the faces, respectively. The CNN models are implemented in Python, and the results are verified by experimenting with multiple benchmark face datasets. The output accuracy obtained from the face detection and recognition is compared with the facial feature detection and recognition to choose the best to identify the criminals. From the comparison, both FDR and FFDR obtained 99.68% accuracy equally