傳統唇語辨識都是用ASM或AAM取得唇形作為參數，可能會遺失部分有用的資訊，本論文考慮唇語的整體影像，利用SIFT作為參數，藉由BOF，可以將多個SIFT特徵點轉化為向量，並利用其訓練HMM模型。我們測試英文字母A~Z，其實驗結果也好於Baseline系統。 Face verification has many applications. The critical problem which lots of researchers concern is how to apply to real-world. In order to robust orientation, translation and scaling of face images, we extract SIFT features of face images which is built dictionary of sparse representation. We propose two kinds of method to extend dictionary via K-means and information theory(extended dictionary and incremental dictionary). Experiments show that we can increase sparseness of sparse coefficients efficiently, also can improve verification rate and reconstruction error via extended dictionary. This paper utilize BCS to solve optimization problem. Compare to OMP algorithm, BCS not only can solve optimization problem but also can improve dictionary by covariance which can decrease uncertainty of observation vectors. Experiments show that incremental dictionary do increases residual of reconstruction error.
Lip reading has utilized ASM or AAM as features past few years. We concern that it might lose some useful information, therefore we consider whole image information by extracting SIFT features. In order to train HMM model via SIFT features, we utilize BOF to transform matrices of SIFT features into vectors. We experiment letters A-Z, and the result show that performance of proposed method is better than baseline systems.
