Face recognition algorithm using extended vector quantization histogram features

Abstract

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

Face Patterns Analysis and Recognition System Based on Quantum Neural Network QNN

“…The use of three databases was employed in training the model, and the databases include, Youtube Faces (YTF), Labelled Faces in the Wild (LFW), and Social Face Classification (SFC) [28]. The authors in [40,41] performed the tasks of extracting pattern recognition through the use of an algorithm referred to as Local Vector Projection Classification (LVPC). Six face databases (UMIST, AR, ORL, Yale B, FERET, and Yale).…”

Fast search real‐time face recognition based on DCT coefficients distribution

“…Clearly, our accuracy‐speed performance is better than the other DCT‐based algorithms. Comparisons with non‐DCT‐based approaches, the vector quantisation (VQ) histogram was proposed in [28]. The histogram is estimated from the face images using the VQ method.…”