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FRIH: A face recognition framework using image hashing

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Abstract

Face recognition is one of the most important research topics in computer vision. Indeed, the face is an important means of communication with humans and it is highly needed for daily contact. Face recognition technology is applied in many biometric applications such as security, video surveillance, access control systems, and forensics. In this technology, hashing has recently made encouraging progress due to its fast retrieval speed and low storage cost. In this work, we propose an effective face recognition framework based on hashing functions. It attempts to leverage a cascaded architecture with two stages of analyzing different visual information based on image hashing. Specifically, we first introduce a filter to overlook a large number of dissimilar identities in terms of local visual information. Similar identities are found quickly through random independent hash functions inspired by Locality Sensitive Hashing (LSH). Next, we further refine candidates and recognize the most similar identities according to global visual information. The global feature is obtained by hashing each face into a high-quality binary feature space using Discrete Cosine Transform (DCT) coefficients. The proposed method is evaluated on three well-known and one combined face dataset. The obtained results, and the provided face recognition application program, demonstrate that the proposed framework improves the recognition rate and significantly reduces recognition time.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. https://www.dropbox.com/sh/fwuexmevandd49s/AABirrubOXbG5yHUZD3SbZT0a?dl=0.

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  1. Faculty of Computer Engineering & IT, Shahrood University of Technology, Shahrood, Iran

    Mahsa Ghasemi & Hamid Hassanpour

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  1. Mahsa Ghasemi
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Correspondence to Hamid Hassanpour.

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Authors have developed an efficient approach for Face Recognition in Large Datasets Using Image Hashing. For the experimental results, authors have considered three public datasets including FERET, ORL, and AR datasets.

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Ghasemi, M., Hassanpour, H. FRIH: A face recognition framework using image hashing. Multimed Tools Appl 83, 60147–60169 (2024). https://doi.org/10.1007/s11042-023-18007-9

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