Facial Image Associative Memory Model

Abstract

Facial image associative memory takes a facial input image and returns associated faces pre-embedded in memory. This paper proposes a three-phase implementation process: a) sensory pre-processing, b) information interfusion, and c) association with existing faces. This paper reports on the simulation and performance of the proposed first phase, sensory preprocessing, based on multiple neural network structures to translate image sensory pre-processing into transformed information. The multi-network structure is tested by 46 faces of 21 individuals. The result shows the first phase can produce acceptable associations at 89.1% of all test faces with less than 0.02 energy error at above 0.60 facial image pixel-based correlation (closeness).

Multi-label imbalanced data enrichment process in neural net classifier training

“…The sampling of the controller has one state sparsely over-sampled relatively to other states due to the application design specification. Other applications, such as facial image associative memory [3] and adaptive self-configurable filters [4], [5], are also compatible with this approach.…”