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Pose invariant non-frontal 2D, 2.5D face detection and recognition technique

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Abstract

In this paper, the Pose Invariant Identity Recognition (PIIR) technique is proposed. It measures face resemblance using facial landmarks, which are further vigorous to pose disparity. The proposed technique utilizes the concepts of face frontalization and discriminative learning techniques. 3D Morphable Model is used in both techniques for pose invariant. The proposed technique is validated on CAS-PEAL face dataset. Experimental results signify that the proposed technique attains authentication accuracy of 90.24% on face datasets with 30° pan-angle. The proposed technique is further applied on 2.5D face dataset with variant facial positions and expressions. Results reveal that the proposed method can identify the face accurately.

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Data availability

The dataset utilized in this study is openly accessible to the public. As such, it can be readily accessed and employed for further research purposes as needed.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Shiva Thavani

  2. Computer Science and Engineering Department, Punjab Engineering College, Chandigarh, India

    Sahil Sharma

  3. Information Technology Department, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Vijay Kumar

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  1. Shiva Thavani
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  2. Sahil Sharma
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  3. Vijay Kumar
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Correspondence to Vijay Kumar.

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Thavani, S., Sharma, S. & Kumar, V. Pose invariant non-frontal 2D, 2.5D face detection and recognition technique. Int. j. inf. tecnol. 15, 2603–2611 (2023). https://doi.org/10.1007/s41870-023-01335-2

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