Application of Hopfield Neural Network for Facial Image Recognition
Neha Soni1, Enakshi Khular Sharma2, Amita Kapoor3
1Neha Soni, Department of Electronic Science, University of Delhi, India.
2Enakshi Khular Sharma, Department of Electronic Science, University of Delhi, India.
3Amita Kapoor, Department of Electronics, SRCASW, University of Delhi, Delhi, India.

Manuscript received on 10 April 2019 | Revised Manuscript received on 16 May 2019 | Manuscript published on 30 May 2019 | PP: 3101-3105 | Volume-8 Issue-1, May 2019 | Retrieval Number: A2816058119/19©BEIESP
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Abstract: Hopfield model (HM) classified under the category of recurrent networks has been used for pattern retrieval and solving optimization problems. This network acts like a CAM (content addressable memory); it is capable of recalling a pattern from the stored memory even if it’s noisy or partial form is given to the model. This paper presents a framework to use HM for machine recognition of human faces. The approach presented here uses Otsu’s method to transform facial images (low and ultra-low-resolution) from grey scale to binary, and Hebb rule to store them in the W (weight matrix) of the network. HM is then tested with up to 45% distortion in facial images; the network is allowed to evolve asynchronously to a stable state. To check positive retrieval, we match (bit-by-bit) the stable state facial image with the original facial image. Our results show 100% retrieval for grey scale facial images (of 60×60 pixel size) for up to 30% distortion. This suggests that HM can be used for face-based security applications when the number of individuals allowed is a limited number like a high-security military area or a lab.
Index Terms: Asynchronous Retrieval, Auto Associative Memory, Face Recognition, Hopfield Model.

Scope of the Article: Image Processing and Pattern Recognition