Keynote Speakers

Hujun Yin (The University of Manchester)

Face Recognition and Challenges

Among various biometric methods,facial image based has certain advantages such as nonintrusive, easily deplorable and highly accurate. Face recognition has found increased applications in security, surveillance, authentication, e-commerce and social networking. Most existing facial recognition methods are appearance-based and reply on facial images for training and recognition. There also exist methods for extracting geometric features of facial images and local facial features for improving recognition performance and/or reducing processing time. This talk gives an introduction to face recognition and its fundamental methods, implementations and applications. The talk will also discuss the challenges in implementation and practical systems.

Face recognition can be cast as a pattern classification and machine learning problem: given a set of facial images labeled with subjects? identities, a classifier is developed or trained; so that when a novel face image from the same group of people (or not) is presented. That is, we seek to identify it from the database. Usually face recognition process involves three steps: face detection, feature extraction and classification. In the first step, the face is detected and located in the image. In the second step, a collection or combination of descriptive measurements or features are extracted from each image. In the third step, a classifier is trained on known samples to assign to each feature vector with subject?s identity. Advanced feature extraction methods such as local binary pattern (LBP) and active shape will also be discussed, along with the roles of advanced feature selection or dimensionality reduction methods and advanced classification techniques such as Support Vector Machines (SVM) popularity and imaging power of mobile devices such as smartphones and tablets has prompted development of numerous new applications on them.

Implementing face recognition onto mobile devices poses a new challenge as the application environment is subject to various uncertainties such as lighting, background and viewing angles, as well as reduced computational power.