PDF-Labeled Faces in the Wild A Database for Studying Face Recognition in Unconstrai

HuangManu Ramesh Tamara Berg and Erik LearnedMi ller Abstract Face recognition has bene64257tted greatly from the many databases that have been produced to study it Most of these databases have been created under controlled conditi ons to facilitat. What shape am I?. I have no flat faces. . I have no straight edges. . I have just one curved face.. I am a …………?. . Well Done! . I am a sphere!. What shape am I?. I have one curved face.. I have one flat face. . using Convolutional Neural Network and Simple Logistic Classifier. Hurieh. . Khalajzadeh. Mohammad . Mansouri. Mohammad . Teshnehlab. Table of Contents. Convolutional Neural . Networks. Proposed CNN structure for face recognition. :. 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. Polyhedra. Walter Whiteley. July 2015. Start with spherical block and hole . polyhedra. Block. Hole. Expanding. Expanding. Contracting. Contracting. (a). (b). (c). (d). Recent Extension. If triangulated sphere has one added cross-beam. . 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 . Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Neeraj Kumar, Alexander C. Berg, Peter N. Belumeur, and Shree K. Nayar. Presented by Gregory Teodoro. Attribute Classification. Early research focused on gender and ethnicity.. Done on small datasets. The inability to recognise familiar objects presented visually is known as visual . agnosia. . There are two main types:. Apperceptive. . agnosia. – a failure to recognise due to impaired visual perception. This implies a physiological problem in the visual system.. 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 . 16/03/2011. 1. Rui. Min. Multimedia Communications Dept.. EURECOM. Sophia . Antipolis. , France. min@eurecom.fr. Abdenour. . Hadid. . Machine Vision Group. University of Oulu. Oulu, Finland. hadid@ee.oulu.fi. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. 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. 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?. Dongyeop. Kang. 1. , Youngja Park. 2. , Suresh . Chari. 2. . 1. . . IT Convergence Laboratory, KAIST . Institute,Korea. 2. . IBM T.J. Watson Research . Center, NY, USA.