Abstract
This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.
Highlights
Face recognition has been a very popular research topic in recent years because of wide variety of application domains in both academia and industry
The training of the Radial basis function (RBF) neural network is done, based on the hybrid learning algorithm (HLA) [17] and we have shown that the proposed feature extraction method with an RBF neural network classifier gives a faster training phase and yields a better recognition rate
This paper presented an efficient method for the recognition of human faces in frontal view of facial images
Summary
Face recognition has been a very popular research topic in recent years because of wide variety of application domains in both academia and industry. This interest is motivated by applications such as access control systems, model-based video coding, image and film processing, criminal identification and authentication in secure systems like computers or bank teller machines, and so forth [1]. The second stage involves extraction of pertinent features from the localized facial image obtained in the first stage. The third stage requires classification of facial images based on the derived feature vector obtained in the previous stage
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