A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

In suspect identification systems, facial features play a crucial role in recognising individuals. However, the challenge lies in sustaining the accuracy of the system over a long period of time, ensuring that it remains consistently high, reliable, and effective. This research introduces a novel lightweight model that requires low trainable parameters, a significantly smaller number than pre-trained models, which use millions of trainable parameters. The newly proposed recurrent feature iterative network integrates a convolutional neural network and long short-term memory in a single structure to synthesise diverse images and to effectively extract facial features. The long short-term memory-based feature-recurrent system demonstrates a significant improvement in accuracy when tested on the augmented reality face database, enhanced extended Yale B, Cohn-Kanade and extended Yale B datasets, achieving encouraging accuracy rates of 99.23%, 99.16%, 98.99%, and 97.68%, respectively. These accuracies, in general, outperform traditional baselines of 68.65%. This research advances the field by providing an innovative solution to enhance suspect identification systems through advanced facial image feature extraction, resulting in significantly improved accuracy rates.