Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture

Abstract

This work addresses the design and implementation of a novel PhotoBiological Filter Classifier (PhBFC) to improve the accuracy of a static sign language translation system. The captured images are preprocessed by a contrast enhancement algorithm inspired by the capacity of retinal photoreceptor cells from mammals, which are responsible for capturing light and transforming it into electric signals that the brain can interpret as images. This sign translation system not only supports the effective communication between an agent and an operator but also between a community with hearing disabilities and other people. Additionally, this technology could be integrated into diverse devices and applications, further broadening its scope, and extending its benefits for the community in general. The bioinspired photoreceptor model is evaluated under different conditions. To validate the advantages of applying photoreceptors cells, 100 tests were conducted per letter to be recognized, on three different models (V1, V2, and V3), obtaining an average of 91.1% of accuracy on V3, compared to 63.4% obtained on V1, and an average of 55.5 Frames Per Second (FPS) in each letter classification iteration for V1, V2, and V3, demonstrating that the use of photoreceptor cells does not affect the processing time while also improving the accuracy. The great application potential of this system is underscored, as it can be employed, for example, in Deep Learning (DL) for pattern recognition or agent decision-making trained by reinforcement learning, etc.

Improved Recognition of Kurdish Sign Language Using Modified CNN

“…5 Frames Per Second (FPS) was sustained across models. The use of photoreceptor cells improved accuracy without affecting processing time [40].…”

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

“…Since photoreceptors have the ability to adapt their response as a function of the surrounding luminance levels, the model of Equation ( 1), modified from the Michaelis-Menten model, is presented, allowing the light range to be normalized between 0 and a maximum value 𝑉 [28]. The outer and inner plexiform layers can be considered a set of biological low-pass and high-pass filters, forming a spatiotemporal filter.…”

“…Since photoreceptors have the ability to adapt their response as a function of the surrounding luminance levels, the model of Equation ( 1), modified from the Michaelis-Menten model, is presented, allowing the light range to be normalized between 0 and a maximum value V max [28].…”