An optical network is described that is capable of recognizing at standard video rates the identity of faces for which it has been trained. The faces are presented under a wide variety of conditions to the system and the classification performance is measured. The system is trained by gradually adapting photorefractive holograms

Li, Hsin-Yu Sidney

Psaltis, Demetri

Qiao, Yong

Caltech Authors

English

2-D shift-invariant volume holographic,&quot;

Adaptive optical networks using photorefractive crystals,&quot;

All-optical errorsignal generation for backpropagation learning in optical multilayer neural networks,&quot;

Applications of holographic storage in lithium niobate,&quot;

Capacity of optical correlators,&quot;

Dynamic holographic memory showing readout, refreshing, and updating capabilities,&quot;

Dynamic photorefractive optical memory,&quot;

Higher-order associative memories and their optical implementations,&quot;

Holographic associative memory with nonlinearities in the correlation domain,&quot;

Holography in artificial neural networks,&quot;

Incremental recording for photorefractive hologram multiplexing,&quot;

Learning in feedforward layered networks-the tiling algorithm,&quot;

Learning internal representations by error propagation,&quot;

Learning, invariance, and generalization in high-order neural networks,&quot;

Limitations on holographic storage capacity of photochromic and photorefractive media,&quot;

Local learning algorithm for optical neural networks,&quot;

Multilayer optical learning networks,&quot;

Multiple storage and erasure of fixed holograms in Fe-doped LiNbO3,&quot;

Optical implementation of radial basis classifiers,&quot;

Optical implementations of radial basis classifiers,&quot;

Periodically refreshed multiply exposed photorefractive holograms,&quot;

Phase-locked sustainment of photorefractive holograms using phase conjugation,&quot;

Signal detection by complex spatial filtering,&quot;

Optical network for real-time face recognition