Titlebook: Audio-and Video-Based Biometric Person Authentication; 4th International Co Josef Kittler,Mark S. Nixon Conference proceedings 2003 Springe

Cpr951

Component-Based Face Recognition with 3D Morphable Modelsf six people and significantly outperformed a comparable global face recognition system. The results show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition based on only three training images of each subject.

期满

A Comparative Study of Automatic Face Verification Algorithms on the BANCA Databaseis superior when a large enough training set is available. Moreover, the SVM is almost insensitive to the choice of representation. However, a dimensionality reduction can be beneficial if constraints on the size of the template are imposed.

synovial-joint

Assessment of Time Dependency in Face Recognition: An Initial Studyed on a different day from the enrolled images, (b) degradation in performance does . follow a simple predictable pattern with time between known and unknown image acquisition, and (c) performance figures quoted in the literature based on known and unknown image sets acquired on the same day may have little practical value.

小画像

吸气

LUT-Based Adaboost for Gender Classificationonverge. This paper presents a novel Look Up Table (LUT) weak classifier based Adaboost approach to learn gender classifier. This algorithm converges quickly and results in efficient classifiers. The experiments and analysis show that the LUT weak classifiers are more suitable for boosting procedure than threshold ones.

Flu表流动

Real-Time Emotion Recognition Using Biologically Inspired Modelspiness, sadness, surprise, or disgust. The three key stages of the architecture are all inspired by biological systems. This emotion recognition system runs in real-time and has a range of applications in the field of humancomputer interaction.

迎合

传授知识

不可救药

Expression-Invariant 3D Face Recognitionsignature images are then decomposed into their principal components. The result is an efficient and accurate face recognition algorithm that is robust to facial expressions. We demonstrate the results of our method and compare it to existing 2D and 3D face recognition algorithms.

平庸的人或物