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Yuntao Shi, Hongfei Zhang, Wei Guo, Meng Zhou, Shuqin Li, Jie Li and Yu Ding
This research proposes a face detection algorithm named LighterFace, which is aimed at enhancing detection speed to meet the demands of real-time community applications. Two pre-trained convolutional neural networks are combined, namely Cross Stage Parti...
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AlsharifHasan Mohamad Aburbeian and Manuel Fernández-Veiga
Securing online financial transactions has become a critical concern in an era where financial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from cyb...
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Zhe Yin, Mingkang Peng, Zhaodong Guo, Yue Zhao, Yaoyu Li, Wuping Zhang, Fuzhong Li and Xiaohong Guo
With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study propos...
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Min Hao, Quan Sun, Chuanzhong Xuan, Xiwen Zhang, Minghui Zhao and Shuo Song
To achieve automated farming management, including the recording, tracking, and statistics of sheep, we harness deep learning technology for sheep face recognition research, and the further development of lightweight sheep face recognition models. Deep l...
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Christine Dewi, Danny Manongga, Hendry, Evangs Mailoa and Kristoko Dwi Hartomo
Face mask detection is a technological application that employs computer vision methodologies to ascertain the presence or absence of a face mask on an individual depicted in an image or video. This technology gained significant attention and adoption du...
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Alvaro A. Teran-Quezada, Victor Lopez-Cabrera, Jose Carlos Rangel and Javier E. Sanchez-Galan
Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long?short-term memory (LSTM) have become a means for providing solutions to problem...
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Jin Su Kim, Cheol Ho Song, Jae Myung Kim, Jimin Lee, Yeong-Hyeon Byeon, Jaehyo Jung, Hyun-Sik Choi, Keun-Chang Kwak, Youn Tae Kim, EunSang Bak and Sungbum Pan
Current advancements in biosignal-based user recognition technology are paving the way for a next-generation solution that addresses the limitations of face- and fingerprint-based user recognition methods. However, existing biosignal benchmark databases ...
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Kalyan Chatterjee, M. Raju, N. Selvamuthukumaran, M. Pramod, B. Krishna Kumar, Anjan Bandyopadhyay and Saurav Mallik
According to global data on visual impairment from the World Health Organization in 2010, an estimated 285 million individuals, including 39 million who are blind, face visual impairments. These individuals use non-contact methods such as voice commands ...
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Shanghao Liu, Chunjiang Zhao, Hongming Zhang, Qifeng Li, Shuqin Li, Yini Chen, Ronghua Gao, Rong Wang and Xuwen Li
A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing met...
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Rong Wang, Ronghua Gao, Qifeng Li and Jiabin Dong
As machine vision technology has advanced, pig face recognition has gained wide attention as an individual pig identification method. This study establishes an improved ResNAM network as a backbone network for pig face image feature extraction by combini...
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