In this paper, a novel Monogenic Sobel Directional Pattern (MSDP) using fractional order masks is proposed for extracting features. The MSDP uses fractional-order Sobel masks to identify thin edges along with color and texture-based information thereby increasing performance. Other edge-detection methods can identify only thick edges. There are three modules namely feature extraction, dimension reduction via a novel discriminant analysis method, and classification using a Convolutional Neural Network (CNN). The proposed MSDP is insensitive to the rotation and scaling changes existing in the images. The Bat Algorithm-based Optimization (BAO) is used for the selection of the best parameters of MSDP. The best value is modified by the Pearson Mutation (PM) operator in an effort to aid the algorithm in avoiding local optima and achieving a balance between global and local searches. The proposed work uses CNN for classification and achieves higher classification accuracy for six datasets.

/pdf/a-novel-monogenic-sobel-directional-pattern-msdp-and-1kq2gq88.pdf

A Novel Monogenic Sobel Directional Pattern (MSDP) and Enhanced Bat Algorithm-Based Optimization (BAO) with Pearson Mutation (PM) for Facial Emotion Recognition

Texture based feature extraction using symbol patterns for facial expression recognition

Automatic Facial Expression Recognition (FER) has become essential today as it has many applications in real time such as animation, driver mood detection, lie detection, and clinical psychology. The effectiveness of FER systems mainly depends on the extracted features. For extracting distinctive features with low dimensions, a new local texture-based image descriptor named Dimensionality Reduced Chess Pattern (DRCP) is proposed for recognizing facial expressions in a person independent scenario. DRCP, an improvement over Chess Pattern (CP), is mainly proposed for effectively reducing the feature vector length of CP. For feature extraction, DRCP also considers the movements of chessmen in a [Formula: see text] neighborhood, as like CP. As a part of feature extraction through DRCP, apart from the center pixel, the remaining 24 pixels are arranged into four groups in such a manner that each group contains the pixels corresponding to three chessmen. From each group, one feature is extracted and thus corresponding to four groups, four features are extracted in a [Formula: see text] neighborhood. The extracted features are fed into multi-class Support Vector Machine (SVM) for expression recognition. The experiments are performed on five “in the lab” datasets (MUG, TFEID, JAFFE, CK[Formula: see text] and KDEF) and on two “in the wild” datasets (RAF and SFEW) in person independent setup to simulate a real world scenario. 

DRCP: Dimensionality Reduced Chess Pattern for Person Independent Facial Expression Recognition

Facial Expression Recognition using Discrete Differential Operator and CNN

Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to assess the strength of steganography schemes. In this paper, a new steganalysis scheme is presented to detect stego-images. Predefined kernels guide the set of a conventional convolutional layer, and the tight cohesion provides completely guided training. The learning rate of convolutional layers with predefined kernels is higher than the global learning rate. The higher learning rate of the convolutional layers with predefined kernels assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels. The Leaky ReLU layer is exploited against the ReLU layer to boost the detection performance. Transfer learning is applied to enhance detection performance. The deep network weights are initialized using the weights of the trained network from high-payload stego-images. The strength of the proposed scheme is verified on the HILL, Mi-POD, S-UNIWARD, and WOW content-adaptive steganography schemes. The results are overwhelming and better than the existing steganalysis schemes.

High-Pass-Kernel-Driven Content-Adaptive Image Steganalysis Using Deep Learning

Local binary descriptors are extensively used for image representation in many of the computer vision applications. A majority of these local binary descriptors exploit the intensity difference of ...

Local Triangular Coded Pattern: A Texture Descriptor for Image Classification

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Local Triangular Coded Pattern: A Texture Descriptor for Image Classification