Structural Geodesic-Tchebychev Transform: An Image Similarity Measure for Face Recognition

Abstract

This work presents a new holistic measure for face recognition. Face recognition involves three steps: Face Detection, Feature Extraction and Matching. In the face detection process to identify the face area in face images, Viola-Jones algorithm has been used. Feature extraction is based on performing double-transformation, where discrete Tchebychev transform is performed on the geodesic distance transform of the grayscale image. Structural Similarity (SSIM) is applied to the resulting image double-transform to find matching factor with other image faces in the FEI (Brazilian) database. Performance is measured using a confidence criterion based on the similarity distance between the recognized person (best match) and the next possible ambiguity (second-best match). Simulation results showed that the proposed approach handles the face recognition efficiently as compared with SSIM.

Levenberg-marquardt backpropagation neural network with techebycheve moments for face detection

“…With different variants such as neural networks [15], convolutional neural networks (CNN), as such: [16]- [18], and deep learning and so on [19], [20]. Exploration techniques rely on layouts in the previous to extract specific features and classification, but there are many strategies for identifying faces by identifying the facial components of the neighborhood and command using factual and geometric models for the human face [21]- [23]. First, low-level review settings to share visual highlights using image properties, eg, edges, power, shading, motion, or general measurements.…”