title: Gabor wavelet associative memory for face recognition.
abstract: This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.
authors.affiliation: Institute for Infocomm Research, Singapore 119613. hhzhang@i2r.a-star.edu.sg
authors.forename: Haihong
authors.initials: H
authors.lastname: Zhang
authors.forename: Bailing
authors.initials: B
authors.lastname: Zhang
authors.forename: Weimin
authors.initials: W
authors.lastname: Huang
authors.forename: Qi
authors.initials: Q
authors.lastname: Tian
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008568:Memory; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*