چكيده به لاتين
Face recognition is one of the practical issues in industrial and security equipment. In this equipment, techniques such as face detection, iris, and facial gestures can be used, which can be specific to one person. Today image processing is the best tool for extracting features and position analysis, and ultimately making the right decisions in this field. In this research, two-dimensional gray face identification was performed using convolutional networks and the use of the wavelet characteristics has been effective in improving the output. The simulation results on the ORL database demonstrate the effectiveness of the proposed method to the previous methods and validates it. The results included 0.898 identity verification and 191.1 for the feature extraction criterion for the proposed method, which was compared with the PCA, CNM, BGLL, and EICA methods in this database.
Keywords: Face Recognition, Convolutional Network, Wavelet Characteristics, Neural Network, Gabor Wavelet.
