Face Recognition Using Hyper Sausage Neuron Networks

Madanny, Shaffan; Samsuryadi,; Yusliani, Novi

doi:10.2991/aisr.k.200424.074

Cited by 1 publication

(2 citation statements)

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“…Several studies have been conducted using various algorithms to recognize faces and facial expressions. In their research, S. Madanny, Samsuryadi and N. Yusliani discussed facial recognition using the hypersausage neural network method [2]. H. Zhi and S. Liu used the Principal Component Analysis (PCA) method to extract features of the gray-scale face, followed by the genetic algorithm to optimize the extracted features and support vector machine as the classifier [3].…”

Section: Introductionmentioning

confidence: 99%

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Design of Real-Time Face Recognition and Emotion Recognition on Humanoid Robot Using Deep Learning

et al. 2022

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A robot is capable of mimicking human beings, including recognizing their faces and emotions. However, current studies of the humanoid robot have not been implemented in the real-time system. In addition, face recognition and emotion recognition have been treated as separate problems. Thus, for real-time application on a humanoid robot, this study proposed a combination of face recognition and emotion recognition. Face and emotion recognition systems were developed concurrently in this study using convolutional neural network architectures. The proposed architecture was compared to the well-known architecture, AlexNet, to determine which architecture would be better suited for implementation on a humanoid robot. Primary data from 30 respondents was used for face recognition. Meanwhile, emotional data were collected from the same respondents and combined with secondary data from a 2500-person dataset. Surprise, anger, neutral, smile, and sadness were among the emotions. The experiment was carried out in real-time on a humanoid robot using the two architectures. Using the AlexNet model, the accuracy of face and emotion recognition was 87 % and 70 %, respectively. Meanwhile, the proposed architecture achieved accuracy rates of 95 % for face recognition and 75 % for emotion recognition, respectively. Thus, the proposed method performs better in terms of recognizing faces and emotions, and it can be implemented on a humanoid robot.

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Section: Introductionmentioning

confidence: 99%

“…The methods mentioned above are quite good at facial recognition and facial expressions, but they have shortcomings such as low accuracy [2][3][4][5][6][7] and have not been implemented in real-time. In addition, face recognition and expression recognition were not combined into a single recognition system.…”

Section: Introductionmentioning

confidence: 99%