Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments

Lina J. Karam; Tong Zhu

doi:10.1117/12.2080393

17 March 2015 Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments

Lina J. Karam, Tong Zhu

Author Affiliations +

Proceedings Volume 9394, Human Vision and Electronic Imaging XX; 93940B (2015) https://doi.org/10.1117/12.2080393
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

The varying quality of face images is an important challenge that limits the eﬀectiveness of face recognition technology when applied in real-world applications. Existing face image databases do not consider the eﬀect of distortions that commonly occur in real-world environments. This database (QLFW) represents an initial attempt to provide a set of labeled face images spanning the wide range of quality, from no perceived impairment to strong perceived impairment for face detection and face recognition applications. Types of impairment include JPEG2000 compression, JPEG compression, additive white noise, Gaussian blur and contrast change. Subjective experiments are conducted to assess the perceived visual quality of faces under diﬀerent levels and types of distortions and also to assess the human recognition performance under the considered distortions. One goal of this work is to enable automated performance evaluation of face recognition technologies in the presence of diﬀerent types and levels of visual distortions. This will consequently enable the development of face recognition systems that can operate reliably on real-world visual content in the presence of real-world visual distortions. Another goal is to enable the development and assessment of visual quality metrics for face images and for face detection and recognition applications.

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Lina J. Karam and Tong Zhu "Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments", Proc. SPIE 9394, Human Vision and Electronic Imaging XX, 93940B (17 March 2015); https://doi.org/10.1117/12.2080393

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