Local Triangular Coded Pattern: A Texture Descriptor for Image Classification

TL;DR: In this article , a new local texture-based image descriptor named Dimensionality Reduced Chess Pattern (DRCP) is proposed for recognizing facial expressions in a person independent scenario, which reduces the feature vector length of CP.

TL;DR: A CNN architecture containing two convolutional, two pooling and two dense layers are utilized with some additional features between the two convolutional layers to generate the information set based feature map.

TL;DR: A new steganalysis scheme is presented to detect stego-images withefined kernels that assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels.

TL;DR: The Cohn-Kanade (CK+) database is presented, with baseline results using Active Appearance Models (AAMs) and a linear support vector machine (SVM) classifier using a leave-one-out subject cross-validation for both AU and emotion detection for the posed data.

TL;DR: This work presents a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition, and improves robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources.

TL;DR: The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input stage and the Gabor representation shows a significant degree of psychological plausibility, a design feature which may be important for human-computer interfaces.

TL;DR: It is shown that CLBP_S preserves more information of the local structure thanCLBP_M, which explains why the simple LBP operator can extract the texture features reasonably well and can be made for rotation invariant texture classification.