Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, compact and discriminative feature extraction from high dimensional hyperspectral image cubes is a challenging task. We propose a spatio-spectral feature extraction method based on the 3D Discrete Cosine Transform (3D-DCT). The 3D-DCT optimally compacts information in the low frequency coefficients. Therefore, we represent each hyperspectral facial cube by a small number of low frequency DCT coefficients and formulate Partial Least Square (PLS) regression for accurate classification. The proposed algorithm is evaluated on three standard hyperspectral face databases. Experimental results show that the proposed algorithm outperforms five current state of the art hyperspectral face recognition algorithms by a significant margin.

No abstract

We propose density independent hydrodynamics model (DIHM) which is a novel and automatic method for coherency detection in crowded scenes. One of the major advantages of the DIHM is its capability to handle changing density over time. Moreover, the DIHM avoids oversegmentation and thus achieves refined coherency detection. In the proposed DIHM, we first extract a motion flow field from the input video through particle initialization and dense optical flow. The particles of interest are then collected to retain only the most motile and informative particles. To represent each particle, we accumulate the contribution of each particle in a weighted form, based on a kernel function. Next, the smoothed particle hydrodynamics (SPH) is adopted to detect coherent regions. Finally, the detected coherent regions are refined to remove the effects of oversegmentation. We perform extensive experiments on three benchmark datasets and compare the results with 10 state-of-the-art coherency detection methods. Our results show that DIHM achieves superior coherency detection and outperforms the compared methods in both pixel level and coherent region level average particle error rates (PERs), average coherent number error (CNE) and F-score.

We propose a novel Gaussian kernel based integration model (GKIM) for anomalous entities detection and localization in pedestrian flows. The GKIM integrates spatio-temporal features for efficient and robust motion representation to capture the distinctive and meaningful information about the anomalous entities. We next propose a block based detection framework by training a recurrent conditional random field (R-CRF) using the GKIM features. The trained R-CRF model is then used to detect and localize the anomalous entities during the online testing stage. We conduct comprehensive experiments on three benchmark datasets and compare the performance of the proposed method with the state-of-the-art anomalous entities detection methods. Our experiments show that the proposed GKIM outperforms the compared methods in terms of equal error rate (EER) and detection rate (DR) in both frame-level and pixel-level comparisons. The frame-level analysis detects the presence of an anomalous entity in a frame regardless of its location. The pixel-level analysis localizes the anomalous entity in term of its pixels.