Algorithms based on Convolution neural networks are significantly better than artificially
designed features. Among these methods, the network trained with the triplet loss function …

Most face recognition algorithms are easily affected by external factors, or require the
recognizer to stand in a fixed position, so the recognition is slow and the accuracy is not very …

Face recognition is difficult due to the higher dimension of face image features and fewer
training samples. Firstly, in order to improve the performance of feature extraction, we …

Abstract Background and Objective Face recognition success rate is influenced by
illumination, expression, posture change, and other factors, which is due to the low …

With the continuous development of deep learning, convolutional neural networks have
achieved good results in the field of face recognition. However, deep convolutional neural …

Face recognition finds its major applications in biometrical security, health care and
marketing. Among many biometrics like finger print, iris, voice, hand geometry, signature …

Deep learning is one of the most important scopes of the Machine Learning that includes
some important architectures. Deep Convolutional Neural Network is one of the attractive …

Face Recognition in today's era became a widespread application in biometric technology
and fo-rensic sciences to identify the individual against the crime involved in the scene. A …

Face recognition technology is a kind of biometric recognition technology that has
developed rapidly in recent years, and the method based on deep learning is one of the …

In order to improve the efficiency of face recognition, a face recognition method based on
principal component analysis and support vector machine is proposed. Principal component …