As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

These days, face recognition systems are widely being employed in various daily
applications such as smart phone unlocking, tracking school attendance, and secure online …

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction
problems as a preprocessing step for machine learning and pattern classification …

Most existing person re-identification (re-id) methods focus on learning the optimal distance
metrics across camera views. Typically a person's appearance is represented using features …

The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has
received extensive attention in research for the less training time, excellent recognition …

Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

Across many fields of social science, machine learning (ML) algorithms are rapidly
advancing research as tools to support traditional hypothesis testing research (eg, through …

Face recognition presents a challenging problem in the field of image analysis and
computer vision, and as such has received a great deal of attention over the last few years …

Many current face recognition algorithms perform badly when the lighting or pose of the
probe and gallery images differ. In this paper we present a novel algorithm designed for …