Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification

Abstract

In the last decade, there has been a surge of interest in addressing complex Computer Vision (CV) problems in the field of face recognition (FR). In particular, one of the most difficult ones is based on the accurate determination of the ethnicity of mankind. In this regard, a new classification method using Machine Learning (ML) tools is proposed in this paper. Specifically, a new Deep Learning (DL) approach based on a Deep Convolutional Neural Network (DCNN) model is developed, which outperforms a reliable determination of the ethnicity of people based on their facial features. However, it is necessary to make use of specialized high-performance computing (HPC) hardware to build a workable DCNN-based FR system due to the low computation power given by the current central processing units (CPUs). Recently, the latter approach has increased the efficiency of the network in terms of power usage and execution time. Then, the usage of field-programmable gate arrays (FPGAs) was considered in this work. The performance of the new DCNN-based FR method using FPGA was compared against that using graphics processing units (GPUs). The experimental results considered an image dataset composed of 3141 photographs of citizens from three distinct countries. To our knowledge, this is the first image collection gathered specifically to address the ethnicity identification problem. Additionally, the ethnicity dataset was made publicly available as a novel contribution to this work. Finally, the experimental results proved the high performance provided by the proposed DCNN model using FPGAs, achieving an accuracy level of 96.9 percent and an F1 score of 94.6 percent while using a reasonable amount of energy and hardware resources.

3D Human Facial Traits’ Analysis for Ethnicity Recognition Using Deep Learning

“…This approach has proven effective in capturing intricate facial features that are critical for distinguishing races, and it continues to drive progress in the field of facial recognition technology. AlBdairi et al [33] developed a DCNN-based 2D race recognition approach to determine the human race, the author used highperformance devices to build a a face recognition system and proposed a technique called field-programmable gate arrays (FPGAs). The periocular area is analyzed for race and gender by Khellat-Kihel et al [34] who proved that deep learning techniques in race prediction still require a large amount of labeled data, and accordingly proposed a DCNN-based predictor to solve several specific biometrics issues on the periocular part of the human faces.…”

Explainable Deep Fake Framework for Images Creation and Classification

“…Biometric security includes face recognition among its categories. Deep learning-based networks are used by face recognition technology [3] [4] to recognize and learn certain face patterns. A mathematical representation is created using the face-related data.…”

Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning

“…In other cases, it has also been employed for classifications or identifications among individuals; for instance, ref. [ 25 ] conducted ethnic identification of participants through the use of DL. By using AI tools in conjunction with advanced image processing within thermal images, it is possible to develop methodologies capable of determining various factors such as emotions or stress state, as demonstrated for [ 8 , 9 ].…”