PPT-Face feature detection using Java and OpenCV

1 Content What is OpenCV What is face detection and haar cascade classifiers How to make face detection in Java using OpenCV Live Demo Problems in face detection process How to improve face detection. CSE 576. Face detection. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Rene Martinez (. CpE. /EE). Trenton Reed (EE). Marlon Smith (. CpE. ). Flashback. Group 22. What is Flashback (Patent Pending). Goals and Motivation. Patent Pending. DVRs are costly. Gain Experience. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. :. A Literature Survey. By:. W. Zhao, R. Chellappa, P.J. Phillips,. and A. Rosenfeld. Presented By:. Diego Velasquez. Contents . Introduction. Why do we need face recognition?. Biometrics. Face Recognition by Humans. A “paradigmatic” method for real-time object detection . Training is slow, but detection is very fast. Key ideas. Integral images. for fast feature evaluation. Boosting. for feature selection. Attentional. Sweta Patil. Guided By:. Mingon Kang. Goal of the Project. To convert a given face image into a funny image by expanding the facial features and adding some filters. . Steps:. Focus the camera on some person, the camera will detect the face. (Courtesy . Boris . Babenko. ). slides adapted from Svetlana . Lazebnik. Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Face Detection. What kind of features?. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Linda Shapiro. ECE P 596. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Review of Rowley face . d. etector. Overview of. . Viola Jones face detector with . The correspondence problem. A general pipeline for correspondence. If sparse correspondences are enough, . choose points for which we will search for correspondences (feature points). For each point (or every pixel if dense correspondence), describe point using a . EE/CSE 576. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Details of Rowley face . d. etector. Review of the basic . AdaBoost. algorithm . The Viola Jones face . AdaBoost. Linda Shapiro. CSE 455. 1. What’s Coming. The basic . AdaBoost. algorithm (next). The Viola Jones face . d. etector features. The modified . AdaBoost. algorithm that is used in Viola-Jones face detection.