Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

Abstract

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification

“…However, additional techniques for DL are still in development and investigation by the researchers, such as the roofline model, graph partitioning, double buffering, and rearranging memory data. While the previous study [46] proposed a network based on the GPU platform, which consumes more power and is unsuitable for portable embedded systems, this research implements a network for human race classification by taking advantage of parallelism techniques on FPGA (type DE1-SoC) to speed up our classification while consuming less power, making it more suitable for portable embedded systems.…”

“…This subsection is devoted to introducing a performance comparison considering several contributions from the SoTA. In recent years, numerous additional studies have attempted to address the classification problems of race [4] and ethnicity [46] by means of DL approaches, e.g., CNNs. Table 1 presents the results provided by several methods of the SoTA dealing with the tackled problem.…”

“…Dense Net This research is an improvement from the previous study [46], which proposed a network based on GPU platform, which requires more power and is unsuitable for portable embedded systems, but in this work, the network for human race classification is implemented by taking advantage of parallelism techniques on FPGA (type DE1-SoC) to speed up our classification while consuming less power.…”

“…In contrast, our network outperforms in terms of accuracy, F1 score, appropriate power usage in FPGA, and processing time in GPU and FPGA, see Figure 7. The FPGA assessments were based on an acceptable amount of DE1-Soc resources, including total logic elements, total block memory, and total logic registers, as shown in Table 4 and This research is an improvement from the previous study [46], which proposed a network based on GPU platform, which requires more power and is unsuitable for portable embedded systems, but in this work, the network for human race classification is implemented by taking advantage of parallelism techniques on FPGA (type DE1-SoC) to speed up our classification while consuming less power.…”

Gesture Recognition of Somatosensory Interactive Acupoint Massage Based on Image Feature Deep Learning Model

“…The CNN is very suitable for image processing. With the speed improvement of computer hardware, the CNN has been widely adopted for face recognition [24,25], gesture recognition [26,27], human pose recognition [28], license plate recognition [29,30], security, and other fields. In this paper, our CNN for pose recognition in acupoint massage includes four parts: convolutional layer, activation layer, pooling layer, and fully connected layer.…”