Abstract:
Single Sample Face Recognition (SSFR) is a biometric technique that identifies
individuals based on only one image of their face. Unlike traditional facial
recognition systems that can use multiple images to enhance accuracy by
capturing various angles, lighting, and expressions, SSFR relies solely on a
single photo. This approach is particularly beneficial in scenarios where
capturing multiple images is difficult or impractical, such as in immigration
control, fugitive tracking, and video surveillance. The accuracy of SSFR is often
impacted by factors like lighting conditions, pose variations, and facial
expressions, making reliable identification more challenging. The paper
proposes a solution to enhance the accuracy of SSFR systems through a multi
resolution analysis method. This approach employs the discrete wavelet
transform (DWT) to extract color-based binarized statistical image features (C
BSIF) across different resolutions. By capturing more detailed information, the
method aims to improve the system's capability to recognize faces under varying
conditions. Experimental analyses conducted on the AR dataset demonstrate
that this technique outperforms several state-of-the-art SSFR methods
