PPT-Face detection Behold a state-of-the-art face detector!

Author : firingbarrels | Published Date : 2020-06-23

Courtesy Boris Babenko slides adapted from Svetlana Lazebnik Face detection and recognition Detection Recognition Sally Consumer application Apple iPhoto httpwwwapplecomilifeiphoto

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Face detection Behold a state-of-the-art face detector!: Transcript

Courtesy Boris Babenko slides adapted from Svetlana Lazebnik Face detection and recognition Detection Recognition Sally Consumer application Apple iPhoto httpwwwapplecomilifeiphoto. 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/. The arbitrator 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. :. Detecting Real-World States with Lousy Wireless Cameras. Benjamin Meyer, . Richard Mietz. , Kay Römer. 1. Introduction. Motivation. Challenges. System Architecture. Evaluation. Structure. 2. Towards the Internet of Things. ATIS Spiking Output. Face Detection. Task: “Is there any part of a face present or not?. (not trying to localize face or identify person). NO. YES. NO. YES. ATIS data collection. Training data: 4 subjects (static cam, . 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. Joseph Miller Michael Osborn Jaclyn . Duket. . Ohn’Jay. Walker. Date. Description. Overview. 2/1/10-2/7/10. Week 1: Introduction to program, research. List several interesting topics. 2/8/10-2/14/10. 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. 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?. 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 . 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.

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