Deep Learning-based Texture Feature Extraction Technique for Face Annotation

Face annotation plays a crucial role in the field of computer vision. Its purpose is to accurately label the faces that appear in an image. The effectiveness of face annotation relies heavily on the representation of facial features, such as color, texture, and shape. Deep texture features, in particular, play a significant role in face annotation systems. It is worth noting that different individuals can possess similar texture features, which can impact the performance of annotation. Therefore, this study addresses the enduring complexity of face similarity by introducing an innovative approach called the Deep Learning-based Texture Feature (DLTF) through the utilization of the efficient deep learning model known as the Residual Network (ResNet). Despite the variations in poses, lighting, expressions, and occlusions that can greatly alter faces, ResNet's deep architecture and feature retention capabilities make it resilient to these changes, ensuring consistent and accurate annotations under diverse conditions. Experimental results obtained from the IMFDB, LFW, and Yahoo datasets demonstrate that the proposed DLTF is the most effective description of deep texture features, leading to improved face naming performance. Furthermore, the proposed DLTF enhances the efficiency of the face-naming task by effectively addressing real-life challenges.