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A vision system to assist visually challenged people for face recognition using multi-task cascaded convolutional neural network (MTCNN) and local binary pattern (LBP)

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Abstract

Visually impaired people are socially disconnected in situations like face-to-face communication and recognize known individuals. Engaging freely with their sighted counterparts is still challenging and adequate attention is not given to non-verbal communication. This work proposed a compact wearable solution to recognize faces to aid the visually impaired in better social interaction. To address this, we develop a portable embedded device with face recognition capabilities, which facilitates a visually impaired person to recognize faces through the audio feedback system. In preprocessing a hybrid method is proposed for enhancing the visual quality of the face. This is based on LAB color space and Contrast Limited Adaptive Histogram Equalization (CLAHE) with gamma enhancement, accurately recognizing the faces irrespective of various illumination conditions. The efficiency of the proposed methodology is evaluated in a real-time scenario with the following parameters: Process CPU usage, process memory usage, Frame per Second (FPS), Model load analysis, and average CPU load analysis. Experimental results show The MTCNN based LPB uses optimal CPU utilization and improve the accuracy of real-time face recognition.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, India

    A. Baskar & T. Gireesh Kumar

  2. Geosystems Research Institute, Mississippi State University, Starkville, MS, 39762, USA

    Sathishkumar Samiappan

Authors
  1. A. Baskar
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  2. T. Gireesh Kumar
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  3. Sathishkumar Samiappan
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Correspondence to A. Baskar.

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Baskar, A., Kumar, T.G. & Samiappan, S. A vision system to assist visually challenged people for face recognition using multi-task cascaded convolutional neural network (MTCNN) and local binary pattern (LBP). J Ambient Intell Human Comput 14, 4329–4341 (2023). https://doi.org/10.1007/s12652-023-04542-8

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  • DOI: https://doi.org/10.1007/s12652-023-04542-8

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