PPT-Partial Face Recognition

Shengcai Liao NLPR CASIA April 29 2015 Background Cooperated face recognition People are asked to stand in front of a camera with good illumination conditions Border pass access control attendance etc. Lauren Ayers. 22.71 . Outline. Partial Dislocations. Why Partials?. Stacking Faults. Lomer-Cottrel. Lock. Force on a Dislocation. Line Tension Model. Dislocation Density. Partial Dislocations. b1. Single unit dislocation can break down into two Shockley partials. Michael O. Williams, M.D., F.A.A.O.S.. Oxford Partial Knee Replacement. The Oxford partial knee replacement is indicated for treatment of . anteromedial. osteoarthritis of the knee. This arthritis involves primarily the medial compartment of the knee with joint space narrowing seen on X-rays. The lateral compartment and patella are usually not arthritic in this condition. The Oxford differs from other partial knees in that it has a mobile UHMWPE bearing which duplicates normal knee kinematics.. TERMINOLOGY. Dentulous Patients. . Is an artificial replacement of an absent art. of the human body. Prosthesis. Edentulous Patients. Patients having a complete set of natural teeth . Patients having all their teeth missing . Weihong Deng (. 邓伟洪. ). Beijing Univ. Post. & Telecom.(. 北京邮电大学. ) . 2. Characteristics of Face Pattern. The facial shapes are too similar, sometimes identical ! (~100% face detection rate, kinship verification). . hongliang. . xue. Motivation. . Face recognition technology is widely used in our lives. . Using MATLAB. . ORL database. Database. The ORL Database of Faces. taken between April 1992 and April 1994 at the Cambridge University Computer . Finding the rule for . partial. from a table of values. X. 1. 2. 4. 6. y. 5. 6. 8. 10. Remember: the rule “looks” like y = . ax. + b. Step 1: find the rate of change. (1,5) (2,6). Feng. . Cen. Outline. R. ecent . advances . in face recognition (FR). Our research work on occluded FR. Face Recognition: applications. Biometrics / access control. No action required. Scan many people at once. Student: . Yikun. Jiang. . Professor: Brendan Morris. Outlines. Introduction of Face Recognition. The . Eigenface. Approach. Relationship to Biology and Neutral Networks. 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition. cogch2 pt 2. 2. Disorders . of Object Recognition. AGNOSIA.  : a general term for a loss of ability to recognize objects, people, sounds, shapes, or smells. . Agnosias result from damage to . cortical areas . Deep Learning for Expression Recognition in Image Sequences Daniel Natanael García Zapata Tutors: Dr. Sergio Escalera Dr. Gholamreza Anbarjafari April 27 2018 Introduction and Goals Introduction Dennis Hamester et al., “Face ExpressionRecognition with a 2-Channel ConvolutionalNeural Network”, International Joint Conference on Neural Networks (IJCNN), 2015. Hao Zhang. Computer Science Department. 1. Problem Statement. Verification. Identification. A. B. Same / Different persons?. A. B. C. D. Which has the same identity as A?. 2. Solutions. Extensions of still face recognition algorithms. Seminars.  . Thursday 10. th. February 2016. Supporting . Children to Disclose Abuse . RECOGNITION. Is there a problem?. No. Recognition. Partial. Recognition. Clear. Recognition. TELLING. Can I talk?. 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 .