An efficient face recognition system based on hybrid optimized KELM

Face recognition (FR) from video offers a challenging issue in the area of image exploration along with computer visualization, furthermore, as such recognized heaps of deem over the previous years on account of its numerous applications in the scope of domains. The chief challenges in the video centered FR are the restraint of the camera hardware, the random poses captured by means of the camera as the subject is noncooperative, and changes in the resolutions owing to disparate lighting conditions, noise along with blurriness. Numerous FR algorithms were generated in the previous decennium, although these approaches are much better, the image’s accuracy is less only. To trounce such difficulties, an efficient FR system centered on hybrid optimized Kernel ELM is proposed. The proposed work encompasses five phases, explicitly (i) preprocessing, (ii) Face detection, (iii) Feature Extraction, (iv) Feature Reduction, and (v) Classification. In the preliminary phase, the data-base video clips are converted in to the frames in which pre-processing are performed utilizing a Modified wiener filter to eliminate the noise. The succeeding phase is employed for detecting the pre-processed image via the viola–jones (V-J). With this technique, the face is identified. After that, the features are extorted. The extracted ones then will be provided as the input to the Modified PCA approach. Then, perform classification operation using hybrid (PSO-GA) optimized Kernel ELM approach. The similar process is replicated for query images (QI). At last, the recognized image is found. Experimental results contrasted with the previous ANFIS classifier and existing methods concerning precision, accuracy, recall, F-measure, sensitivity along with specificity. The proposed FR system indicates better accuracy when compared with the prevailing methods.