• Miss. Shubhangi Sunil Satav
• Prof. Jayaprabha Kanase

face detection has been a challenging issue within the areas of image processing and pattern recognition. a brand new external body part detection algorithm by employing a Haar-cascade classifier is proposed during this paper. First, images of individuals are processed by a primitive Haar cascade classifier, nearly without wrong face rejection (very low rate of falsenegative) but with some wrong acceptance (false positive). Secondly, to urge eliminate these wrongly accepted non-human faces, a weak classifier supported face skin hue histogram matching is applied and a majority of non-human faces are removed. Next, another weak classifier supported eyes detection is appended and a few residual non-human faces are determined and rejected. Finally, a mouth detection operation is used on the remaining non-human faces, and also the false positive rate is further decreased. With the assistance of OpenCV, test results on images of individuals under different occlusions and illuminations and a few degrees of orientations and rotations, in both training set and test set show that the proposed algorithm is effective and achieves state-of-the art performance. Furthermore, it's efficient due to its easiness and ease of implementation. The accuracy and F1 score are found to be increase.

Haar Cascade Algorithm, Face Detection, Face Recognition, Gray Conversion

"Human Face Detection Algorithm via Hybrid Haar Cascade Classifier Review ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b845-b847, June-2021, Available :http://www.jetir.org/papers/JETIR2106261.pdf

"Human Face Detection Algorithm via Hybrid Haar Cascade Classifier Review ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb845-b847, June-2021, Available at : http://www.jetir.org/papers/JETIR2106261.pdf