Face recognition based on facial feature training

The detection and extraction of characteristic features of the human face are primordial tasks in any given approach for face recognition. Within this context, the present paper will present a comprehensive list of steps that can help obtain the major discriminative features of the human face with the abstraction of the fuzzy data that are influenced by several external factors, notably light conditions, which often disrupt the results obtained by classifiers for human face recognition. The approach proposed in this paper can be considered a potentially strong candidate for use in a variety of commercial and industrial applications, particularly those related to security. In fact, in addition to its usefulness in identification processes, this face recognition system is also of particular importance for those who are interested in search and navigation processes in online video masses. In essence, our process was based on a corpus that contained a huge number of faces acquired in different positions and various lighting conditions. The identification and classification of faces was achieved through the use of a neural network called the multi-layer perceptron (MLP), which is one of the most common networks used in this context.