'Face images' presentation slideshows

Face Recognition and Feature Subspaces

03/09/10. Face Recognition and Feature Subspaces. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Some slides from Lana Lazebnik, Silvio Savarese, Fei-Fei Li. Object recognition. Last Class Object instance recognition: focus on localization of miscellaneous objects

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Face Recognition and Feature Subspaces

03/10/11. Face Recognition and Feature Subspaces. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Presented by Ali Farhadi. Some slides from Lana Lazebnik, Silvio Savarese, Fei-Fei Li. Object recognition. Last Class

669 views • 55 slides

Window-based models for generic object detection

Window-based models for generic object detection. Mei-Chen Yeh 04/24/2012. Object Detection. Find the location of an object if it appear in an image Does the object appear? Where is it?. Viola-Jones face detector. P. Viola and M. J. Jones. Robust Real-Time Face Detection. IJCV 2004.

683 views • 52 slides

NEURAL NETWORK-BASED FACE DETECTION

NEURAL NETWORK-BASED FACE DETECTION . BY GEORGE BOTROUS PHANI SIDDAPAREDDY RAKESH REDDY KATPALLY VIJAYENDAR REDDY DAREDDY. Introduction.

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Face Detection and Recognition Readings: Ch 8: Sec 4.4, Ch 14: Sec 4.4

Face Detection and Recognition Readings: Ch 8: Sec 4.4, Ch 14: Sec 4.4. Bakic flesh finder using color (HW4) Fleck, Forsyth, Bregler flesh finder using color/texture and body parts (the naked people paper) Rowley & Kanade face detector using neural nets to

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ForSe Overview

ForSe Overview. Forensics and Security Laboratory (ForSe Lab) School of Computer Engineering Nanyang Technological University. Mission of ForSe Lab.

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A Brief Introduction on Face Detection

A Brief Introduction on Face Detection. Mei-Chen Yeh 04/06/2010. P. Viola and M. J. Jones, Robust Real-Time Face Detection, IJCV 2004. Object Detection. Find the location of an object if it appear in an image Does the object appear? Where is it?. Applications. Applications.

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NMF Demo: Lee, Seung

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Triangle-based approach to the detection of human face

Triangle-based approach to the detection of human face. Source ? Pattern Recognition, Vol. 34, Issue 6, June 2001, pp. 1271-1284 Authors ? Chiun-Hsiun Lin, Kuo-Chin Fan Speaker ? Chia-Chun Wu ( ??? ) Date ? 2004/11/25. Outline. Introduction Proposed system

388 views • 28 slides

Kullback-Leibler Boosting

Kullback-Leibler Boosting. Ce Liu, Hueng-Yeung Shum Microsoft Research Asia CVPR 2003 Presented by Derek Hoiem. RealBoost Review. Start with some candidate feature set Initialize training sample weights Loop: Add feature to minimize error bound

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Facial Feature Extraction by Kernel Independent Component Analysis

Facial Feature Extraction by Kernel Independent Component Analysis. ?? : ???. Outline. Introduction Independent Components Analysis Image data Experimental results References. Introduction.

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HYBRID-BOOST LEARNING FOR MULTI-POSE FACE DETECTION AND FACIAL EXPRESSION RECOGNITION

312 views • 16 slides

Automated Facial Recognition:

Automated Facial Recognition:. Overview and Applications by Brandon Hume. Human vs. Computer Vision. Computer vision still has a long way to go to match the capabilities of human face recognition. People use non-face features to aid in recognition.

455 views • 16 slides

Face Recognition: A Comparison of Appearance-Based Approaches

Face Recognition: A Comparison of Appearance-Based Approaches. Thomas Heseltine Advanced Computer Architecture Group Department of Computer Science - University of York. www.cs. york .ac.uk/~tomh. tom.heseltine@cs.york.ac.uk. Introduction. Growing interest in biometric authentication

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401 views • 15 slides

Zen and the Art of Facial Image Quality

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FDDB: A Benchmark for Face Detection in Unconstrained Settings

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FACE IMAGE RETRIEVAL BY PROJECTION-BASED FEATURES

FACE IMAGE RETRIEVAL BY PROJECTION-BASED FEATURES. Chaur-Chin Chen, Yu-Shu Shieh, Hsueh-Ting Chu Department of Computer Science Institute of Information Systems & Applications National Tsing Hua University, Hsinchu,Taiwan 30013 E-mail: cchen@cs.nthu.edu.tw. ABSTRACT.

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Digital Image Processing Project

Digital Image Processing Project. Group-1 Group members- Sadbodh sharma-y07uc101 Kapil Phatnani-y08uc065. Face Recognition. Input: Input will be a live video stream of human faces . Output:

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Hemispheric Specialization and Interhemispheric Interaction in Self-Recognition

Hemispheric Specialization and Interhemispheric Interaction in Self-Recognition. 86.12. Lucina Uddin, Janice Rayman, and Eran Zaidel Department of Psychology, University of California Los Angeles. Introduction. Results. Summary of Main Findings.

121 views • 1 slides