MFAST Processing Model for Occlusion and Illumination Invariant Facial Recognition

Abstract

Illumination Variation and wearable objects loses the partial facial information that it degrades the accuracy of recognition process. In this paper, a high performance driven accurate method is provided for facial recognition. The proposed MFAST (Multi-Featured Analog Signal Transformed) Model genuinely transmute the substantial facial information in analog featured conformation. This analog featured structured is formed using segmented featured elicitation. These features include center difference evaluation as moment, the asymmetric structure analysis as Skewness and Outlier Prone Measure as Kurtosis. These analogous features are shaped to justified form and generate a compound signal form. Mapping of these distillates signal points over facial dataset with specification of threshold window. The decomposed form recognition method enhanced the accuracy and performance. The experimentation on FERET, LFW and Indian Databases signify that the model outperformed than existing algorithms.

Multi Featured Fuzzy based Block Weight Assignment and Block Frequency Map Model for Transformation Invariant Facial Recognition

“…The transformation of the image in occlusion and illumination ineffective signal [26] forms to improve the robustness of facial recognition. The ring [27] segmentation method was applied to take dynamic and feature adaptive decision to handle the contrast issue in facial recognition.…”

“…Face expression [24] classification, individual identification of group [29] [25], gender identification, age-estimation are the most common application worked by the researchers in recent years. These applications are having the challenges of occlusion [25][26], illumination [19][21] [26] variance, contrast [27] unbalancing, head or pose [28] precise variations, etc. The researchers have provided numerous algorithms to rectify the images against these challenges or to extract the variant robust features so that the accuracy of face recognition can be improved in existence of these challenges.…”

“…The researchers have provided numerous algorithms to rectify the images against these challenges or to extract the variant robust features so that the accuracy of face recognition can be improved in existence of these challenges. The facial region [29] extraction, noise rectification, color, contrast and alignment specific adjustments were integrated in preprocessing stage to transform the facial image to normalized form [26][27][28] [29]. Various statistical [24], textural [20][21] [25] and other variance robust feature descriptors were applied by the researchers to represent the face with reduced and effective dimensions.…”