基于邊緣計算中極端姿態(tài)和表情的人臉識別
2021年電子技術應用第6期
況朝青1,2,,3,,賀 超1,2,,3,,王均成1,,2,3,,鄒建紋1,,2,3
1.重慶郵電大學 通信與信息工程學院,,重慶 400065,;2.重慶高校市級光通信與網絡重點實驗室,重慶 400065,; 3.泛在感知與互聯重慶市重點實驗室,,重慶 400065
摘要: 隨著信息技術的發(fā)展,人臉識別在支付,、工作和安防系統(tǒng)中應用的越來越多,。在邊緣計算系統(tǒng)中,為了處理的速度,,通常選擇較小的神經網絡進行人臉識別,,這樣會導致識別率低。并且在實際應用中大多都是對于圖片質量較高的人臉可以很好地識別,,但對于受光照影響較大,、表情和姿態(tài)變化大的圖片識別率不是很高。因此,,選擇SqueezeNet輕量級網絡,,該網絡層數小,可以很好地運用于邊緣計算系統(tǒng)中,。采用了預處理的方法來對圖片進行預處理,,然后改進了SqueezeNet網絡的損失函數以及加入了ResNet網絡中的殘差學習方法。最后通過對LFW和IJB-A數據集進行測試,,該研究方法明顯提高了識別率,。
中圖分類號: TN911.73;TP391.4
文獻標識碼: A
DOI:10.16157/j.issn.0258-7998.200968
中文引用格式: 況朝青,,賀超,王均成,,等. 基于邊緣計算中極端姿態(tài)和表情的人臉識別[J].電子技術應用,,2021,47(6):30-34.
英文引用格式: Kuang Chaoqing,,He Chao,,Wang Juncheng,et al. Face recognition with extreme posture and expression[J]. Application of Electronic Technique,,2021,,47(6):30-34.
文獻標識碼: A
DOI:10.16157/j.issn.0258-7998.200968
中文引用格式: 況朝青,,賀超,王均成,,等. 基于邊緣計算中極端姿態(tài)和表情的人臉識別[J].電子技術應用,,2021,47(6):30-34.
英文引用格式: Kuang Chaoqing,,He Chao,,Wang Juncheng,et al. Face recognition with extreme posture and expression[J]. Application of Electronic Technique,,2021,,47(6):30-34.
Face recognition with extreme posture and expression
Kuang Chaoqing1,,2,3,,He Chao1,,2,3,,Wang Juncheng1,,2,3,,Zou Jianwen1,,2,3
1.School of Communication and Information Engineering,,Chongqing University of Posts and Telecommunications,, Chongqing 400065,China,; 2.Optical Communications and Networks Key Laboratory of Chongqing,,Chongqing 400065,China,; 3.Ubiquitous Sensing and Networking Key Laboratory of Chongqing,,Chongqing 400065,China
Abstract: With the development of information technology, face recognition is used more and more in payment, work and security system. In the edge computing system, in order to deal with the speed, we usually choose a smaller neural network for face recognition, which may cause the recognition rate is not very high. And in practical applications, most of them can recognize the face with high image quality, but the recognition rate is not very high for the face which is greatly affected by the light and has great changes in expression and posture. Therefore, this paper chooses the SqueezeNet lightweight network, which has a small number of layers and can be well used in edge computing system. The method of preprocessing is used to preprocess the image, and then the loss function of SqueezeNet network and the residual learning method of ResNet network are improved. Finally, through the test of LFW and IJB-A data set, it is concluded that the research method in this paper can significantly improve the recognition rate.
Key words : neural network,;face recognition,;preprocessing;SqueezeNet network,;ResNet network
0 引言
近年來,,人臉識別受到越來越多的關注,主要是通過神經網絡模型來進行人臉識別,。但人臉識別依然是一個非常重要但又極具挑戰(zhàn)性的問題,,主要是現在大部分的人臉識別采用的圖像都是靜態(tài)和質量較高的圖片,所以識別效果很好,。但在實際應用中,,人臉圖像受到光照、表情和較大的姿態(tài)變化的影響,,可能導致識別率急劇下降,。因此,采用一種預處理的方式來處理圖片,,提高圖片的質量,,成為了當下研究的關鍵[1]。并且在邊緣計算系統(tǒng)中,,采用大型網絡來進行人臉識別是不現實的,,主要是受到處理器的速度和功耗的影響,,因此這方面的應用成為了研究的熱點。
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作者信息:
況朝青1,,2,3,,賀 超1,,2,3,,王均成1,,2,3,,鄒建紋1,,2,3
(1.重慶郵電大學 通信與信息工程學院,,重慶 400065,;2.重慶高校市級光通信與網絡重點實驗室,重慶 400065,;
3.泛在感知與互聯重慶市重點實驗室,,重慶 400065)
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