Title :
Adaptive activation function neural net for face recognition
Author :
Talukder, Ashit ; Casasent, David
Author_Institution :
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA
Abstract :
An efficient two-stage algorithm to compute nonlinear features is described. Its implementation on a neural net with adaptive activation functions that raise the input data to an arbitrary power is described. Its use in face recognition with unknown input poses is presented
Keywords :
covariance matrices; face recognition; neural nets; transfer functions; adaptive activation function neural net; face recognition; nonlinear features; two-stage algorithm; unknown input poses; Closed-form solution; Covariance matrix; Face recognition; Image reconstruction; Neural networks; Power engineering computing; Principal component analysis; Propulsion; Standards development; Symmetric matrices;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939081
