An Enhanced Face Detection System using A Novel FIS-CDNN Classifier

A computer application that can detect, track, identify or verify human faces as of an image video capture utilizing a digital camera is Face Recognition (FR). Few challenges like low-resolution images, aging, uncontrolled pose, illumination changes, and poor lighting conditions are not tackled even though huge advancement has been created in the Detection (FDR) domain. Utilizing Modified Tiny (MTFD) Fuzzy Interference System - Convolutional Deep Neural Network (FIS-CDNN) classifier, (FRS) was proposed here to tackle all complications. Primarily, Gamma correction Based Histogram Equalization (GBHE) technique utilized augment image’s input pre-processing phase. The MTFD employed detect face. Following that, features extracted. Improved Chehra (IC) landmark extraction method retrieve features. And finally, Geometric Features (GFs) Later, Gaussian centered Spider Monkey Optimization (GSMO) Algorithm choose vital To recognizing face, chosen fed into FIS-CDNN classifier. When analogized prevailing models, it concluded via experiential outcomes higher accuracy attained by method.