Automatic recognition of facial expression is an active research topic in computer vision due to its importance in both human-computer and social interaction. One of the critical issues for a successful facial expression recognition system is to design a robust facial feature descriptor. Among the different existing methods, the Local Binary Pattern (LBP) has been proved to be a simple and effe...

This paper proposes a novel local feature descriptor, Local Directional Number Pattern (LDN), for face analysis: face and expression recognition. LDN encodes the directional information of the face’s textures (i.e., the texture’s structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask, ...

Probabilistic models with hidden variables such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have recently become popular for solving several image content analysis tasks. In this work we will use a pLSA model to represent images for performing scene classification. We evaluate the influence of the type of local feature descriptor in this context and co...

In this paper, we propose a novel local feature descriptor, Local Gaussian Structural Pattern (LGSP), for face recognition that encodes the directional information of the face’s textures (i.e., the texture’s structure), producing a more discriminant code than other state-of-the-art methods. Each micro-pattern’s structure is computed by using a derivative-Gaussian compass mask, which is more rob...

Humans are extremely proficient at perceiving natural scenes and understanding their contents. Scene recognition in Human is the natural activity by which human can easily recognize the scene even if the scene is complex, partially occluded or blurred. In machine vision the recognition rate is less compared with human vision. To improve the recognition rate of the machine vision an efficient st...

In this paper, we present local image descriptor using VQ-SIFT for more effective and efficient image retrieval. Instead of SIFT's weighted orientation histograms, we apply vector quantization (VQ) histogram as an alternate representation for SIFT features. Experimental results show that SIFT features using VQ-based local descriptors can achieve better image retrieval accuracy than the conventi...

Face recognition is becoming very popular tools for a successful human commuter interaction system. It seems to be a good compromise between reliability and social acceptance and balances security and privacy well. In this paper, we have presented a new appearance-based feature descriptor, the local directional pattern Variance (LDPv), to represent facial components and analyzed its performance...

Local feature detection and description have gained a lot of interest in recent years since photometric descriptors computed for interest regions have proven to be very successful in many applications. In this paper, we propose a novel interest region descriptor which combines the strengths of the well-known SIFT descriptor and the LBP texture operator. It is called the center-symmetric local b...

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous intera...