GPU Enabled Face Recognition System using Convolutional Neural Network
Abstract
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Faces represent complex multidimensional meaningful visual stimuli and developing a computational model for face recognition is difficult. We present a hybrid neural-network solution which compares favorably with other methods. The system combines local image sampling, a self-org
Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set
We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image sampl
In this paper, a hybrid system is presented in which a convolutional neural network (CNN) and a Logistic regression classifier (LRC) are combined. A CNN is trained to detect and recognize face images, and a LRC is used to classify the features learned by the convolutional network
In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the wei
This chapter addresses an improved approach to video face recognition (VFR). Techniques to recognize faces in video streams have been described in the literature for more than 20 years (Wang et al., 2009). Early methods were based on the still-to-still techniques which aimed at s