Face Detection And Gender Classification App
Harsh Bansal, Vineet Kaur, Sandeep Vashishtha, Prachi Mishra
Facial recognition, mood detection, artificial intelligence, personalized experience, physiological cues, Open CV potential risks, backlit, LWS
Face Detection app for gender classification from camera is a challenging task due to the variability of human faces, the pose of the face, and the lighting conditions. A face detection app for gender classification built on Python programming language was proposed. The app uses the OpenCV and dlib libraries to detect faces, extract features, and classify the extracted features using a machine learning algorithm. The app achieved an accuracy of 95% on the LFW dataset. The app was implemented in Python and tested on a variety of images. The app was evaluated on the LFW dataset. The app has the following limitations: it is not able to accurately classify faces that are in low-light or that are backlit, and it is not able to accurately classify faces that are wearing sunglasses or hats. Future work on the app will focus on improving the accuracy of the app in these challenging conditions.
Article Details
Unique Paper ID: 160957

Publication Volume & Issue: Volume 10, Issue 2

Page(s): 149 - 153
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