The PhD (Pretty helpful Development functions for) face recognition toolbox is a collection of Matlab functions and scripts intended to help researchers working in the field of face recognition. The toolbox includes implementations of some of the most popular face recognition techniques, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA), and Kernel Fisher Analysis (KFA). It features functions for Gabor filter construction, Gabor filtering, and all other tools necessary for building Gabor-based face recognition techniques. In addition to the listed techniques there are also a number of evaluation tools available in the toolbox, which make it easy to construct performance curves and performance metrics for the face recognition technique you are currently assessing. These tools allow you to compute ROC (Receiver Operating Characteristics) curves, EPC (Expected performance curves) curves, and CMC (cumulative match score curves) curves. Most importantly (especially for beginners in this field), the toolbox also contains several demo scripts that demonstrate how to build and evaluate a complete face recognition system. The demo scripts show how to align the face images, how to extract features from the aligned, cropped and normalized images, how to classify these features and finally how to evaluate the performance of the complete system and present the results in the form of performance curves and corresponding performance metrics. The toolbox, which is currently available from its on-line repositories on Matlab Central, the face recognition homepage (http://www.face-rec.org/) and the toolbox’s homepage (http://luks.fe.uni-lj.si/sl/osebje/vitomir/face_tools/PhDface/), was downloaded several hundred times since its introduction in 2012. The toolbox ships with a user manual, which is also listed in the Slovenian bibliographic database COBISS [COBISS.SI-ID 8967764].
F.23 Development of new system-wide, normative and programme solutions, and methods
COBISS.SI-ID: 8967508The alignment of the facial region with a predefined canonical form is one of the most crucial steps in a face recognition system. Most of the existing alignment techniques rely on the position of the eyes and, hence, require an efficient and reliable eye localization procedure. In the lecture we introduce a novel technique for this purpose, which exploits a new class of correlation filters called Principal directions of Synthetic Exact Filters (PSEFs). The proposed filters exhibit desirable properties, such as relatively short training times, computational simplicity, high localization rates and real time capabilities. We present the theory of PSEF filter construction, elaborate on their characteristics and finally develop an efficient procedure for facial landmark localization using several PSEF filters. The effectiveness of the developed technique is demonstrated on the task of eye localization using more than 40000 facial images pooled from the FERET and LWF databases. The results of our experiments suggest that the PSEF filters produce significantly better localization results than, for example, the Haar-cascade object detector, while ensuring a more than 10-fold improvement in the processing time. The lecture is based on the ERK paper »Advanced correlation filters for facial landmark localization« [COBISS.SI-ID 9381716].
B.04 Guest lecture
COBISS.SI-ID: 9402452The deliverable presents a summary of the research work conducted within work package 2 (WP2) of the postdoctoral project BAMBI, where the focus of our research work was directed on procedures for face detection and registration. The deliverable consists of two parts, out of which the first describes our work and findings on the problem of face detection, while the second describes the work and findings on the problem of robust face registration.
F.01 Acquisition of new practical knowledge, information and skills
COBISS.SI-ID: 9666132