PPT-Dictionary Representation of Deep Features for Robust Face

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. Yang Member IEEE Arvind Ganesh Student Member IEEE S Shankar Sastry Fellow IEEE and Yi Ma Senior Member IEEE Abstract We consider the problem of automatically recognizing human faces from frontal views with varying expression and illuminationaswel Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . 1 Speeded Up Robust Features (SURF) INDEX Abstract .. Enjoy with hearty pasta dishes and roast meats.. Rich and robust, deep red in color with flavors of black currant on the palate.. Enjoy with hearty pasta dishes and roast meats.. Rich and robust, deep red in color with flavors of black currant on the palate.. .. Enjoy with hearty pasta dishes and roast meats.. Rich and robust, deep red in color with flavors of black currant on the palate.. Enjoy with hearty pasta dishes and roast meats.. Rich and robust, deep red in color with flavors of black currant on the palate.. 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). Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . IST597: Foundations of Deep Learning. The Pennsylvania State . University. Thanks to . Sargur. N. Srihari, . Rukshan. . Batuwita. , . Yoshua. . Bengio. Manual & Exhaustive Search. Manual Search. Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . 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. Lili Sun, Proof School. Arijit. Das, Computer Science. Results . From preliminary analysis we have gotten the following from appending different layers together. In total we have around 40 layers. For graph visualization we are using . (Smith et al., 2008; Morgan et al., 2008; Lu et al., 2011) and JNLPBA (Kim et al., 2004), dozens of new solu-tions emerged for NER (e.g. Campos et al., 2013) and for normali-zation (Wermter et al., 20 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?.