Open AccessHeterogeneous face recognition based on modality‐independent Kernel Fisher discriminant analysis joint sparse auto‐encoder
Author(s) -
Hu Weipeng,
Hu Haifeng
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.2661
Subject(s) - pattern recognition (psychology) , artificial intelligence , facial recognition system , kernel (algebra) , computer science , linear discriminant analysis , feature (linguistics) , face (sociological concept) , kernel fisher discriminant analysis , projection (relational algebra) , autoencoder , mathematics , algorithm , artificial neural network , social science , linguistics , philosophy , combinatorics , sociology
A novel method called modality‐independent Kernel discriminant analysis joint sparse auto‐encoder, for solving heterogeneous face recognition problem is proposed. A projection matrix to map multimodal data into a common feature space for representing cross‐modal image data is first learnt. Then extend the model via sparse auto‐encoder in an unsupervised manner with the combination of a regularisation term and a Kullback–Leiber divergence term. Different from classical approaches, this model does not require the data correspondences when collecting external cross‐modal data. Thus, it is practical for real‐world cross‐modal classification problem. Experiments conducted on two heterogeneous face datasets demonstrate the effectiveness of the proposed approach.