Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System
Showkat Ahmad Dar, S Palanivel
Advances in Science, Technology and Engineering Systems
Face Recognition (FR) is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along
... classifiers are used for the recognition of faces. Existing Machine Learning (ML) methods are failed to achieve optimal performance due to their inability to accurately extract the features from the face image, and enhancing system's recognition accuracy system becomes very difficult task. Majority of designed FR systems has two major steps: extraction of feature and classifier. Accurate FR system is still a challenge, primarily due to the higher computational time and separate feature extraction. In general, for various applications using images, deep learning algorithms are mostly recommended for solving these problems because it performs combined feature extraction and classification task. Deep learning algorithm reduces the computation time and enhances the recognition accuracy because of automatic extraction of feature. The major novelty of the work is to design a new VGG-16 with Transfer Learning algorithm for face recognition by varying active layers with three levels (3, 4, and 7). It also designs the Convolutional Neural Network (CNN) for FR system. The proposed work introduced a new Real Time Face Recognition (RTFR) system. The process is broken into three major steps: (1) database collection, (2) FR to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92. Second step, efficient RTFR algorithm is then used to recognize faces with a known database. Here two different deep learning algorithms such as CNN and VGG-16 with Transfer Learning are introduced for RTFR system. This proposed system is implemented using Keras. Thirdly the performance of these two classifiers is measured using of precision, recall, F1-score, accuracy and k-fold cross validation. From results it concludes that proposed algorithm produces higher accuracy results of 99.37%, whereas the other existing classifiers such as VGG3, VGG7, and CNN gives the accuracy results of 75. 71%, 96.53%, and 69.09% values respectively.