Wissam J. Baddar

  • Citations Per Year
Learn More
Recognizing spontaneous micro-expression in video sequences is a challenging problem. In this paper, we propose a new method of small scale spatio-temporal feature learning. The proposed learning method consists of two parts. First, the spatial features of micro-expressions at different expression-states (i.e., onset, onset to apex transition, apex, apex to(More)
In this paper, we propose a new facial landmarks detection method based on deep learning with facial contour and facial components constraints. The proposed deep convolutional neural networks (DCNNs) for facial landmark detection consists of two deep networks: one DCNN is to detect landmarks constrained on the facial contour and the other is to detect(More)
In this paper, we propose bilateral features for classifying breast masses by extracting the asymmetric information of both the left and the right breasts in the digital breast tomosynthesis (DBT) reconstructed volume. Clinically, it is known that the left and the right breast of the same patient tend to present a high degree of symmetry of internal(More)
In this paper, we propose a novel normal breast tissue removal approach using sparse representation (SR), in order to emphasize subtle microcalcifications (MCs) for MC cluster (MCC) detection in mammograms. The proposed method adopts SR to estimate normal breast tissue texture only; such that the difference between estimated image and the original image can(More)
We propose a bilateral hemiface feature representation learning via convolutional neural networks (CNNs) for pose robust facial expression recognition. The proposed method considers two characteristics of facial expressions. First, features from local patches are more robust to pose variations. Second, human faces are bilaterally symmetrical on left and(More)
In this paper, we propose a new logarithmic filter grouping which can capture the nonlinearity of filter distribution in shallow CNNs. Residual identity shortcut is incorporated with the filter grouping to enhance the performance of shallow networks. The proposed logarithmic filter grouping is evaluated using a compact CNN structure with logarithmic group(More)
Human action recognition (HAR) has been attracting much attention in the computer vision arena. In particular, many research efforts were dedicated for developing discriminative feature extraction methods for improving the HAR performance. Among them, trajectory-based features have shown state-of-the-art performance. However, the time-variance of(More)
  • 1