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Crowd saliency prediction refers to predicting where people look at in crowd scene. Humans have remarkable ability to rapidly direct their gaze to select visual information of interest when looking at a visual scene. Until now, research efforts are still focused on that which type of feature is representative for crowd saliency, and which type of learning model is the robust one for crowd saliency...
A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify...
The last research efforts made in the face recognition community have been focusing in improving the robustness of systems under different variability conditions like change of pose, expression, illumination, low resolution and occlusions. Occlusions are also a manner of evading identification, which is commonly used when committing crimes or thefts. In this work we propose an approach based on the...
Robust face detection is one of the most important preprocessing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D facial models, etc. Although this topic has been intensely studied for decades, it is still challenging due to numerous variants of face images in real-world scenarios. In this paper, we present a novel approach named Multiple...
This paper presents a robust hand detection algorithm using the facial information. The proposed algorithm consists of four steps: (i) detection of a face, (ii) generation of regions of interest (ROI) to detect hands, (iii) skin color extraction from the face region, and (iv) detection of hands using the face skin color in the ROI. The proposed algorithm can reduce false detection caused by a similar...
Existing face recognition methods suffer from efficiency problems and heavily rely on proper features extraction. In this paper, we propose an efficient face classification method which aims to reduce sensitivity to facial variations and occlusions, meanwhile complete tasks efficiently. In contrast with most energy minimizing based recognition methods, proposed algorithm is cast as a simple classification...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
This paper presents a novel pose-indexed based multi-view (PIMV) face alignment framework. Most of the current cascaded regression face alignment methods generally start with a mean shape. However, when the initial shape is far from the ground truth, the performance significantly deteriorates. Our approach aims to obtain a preferable initial shape from a pose-indexed shape searching space. This space...
Existing face hallucination methods are optimized to super-resolve uncompressed images and are not able to handle the distortions caused by compression. This work presents a new dictionary construction method which jointly models both distortions caused by down-sampling and compression. The resulting dictionaries are then used to make three face super-resolution methods more robust to compression...
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the ability to handle non-stationary outliers, i.e. outliers which have time varying support. We validate our approach with empirical results showing the superiority of the proposed method over competing approaches in synthetic...
This paper investigates precise pupil center localization in low-resolution images. Being an essential preprocessing step in many applications such as gaze estimation, face alignment as well as human-computer interaction, robust, precise, and efficient methods are necessary. We present a method for accurate eye center localization operating with images from simple off-the-shelf hardware such as webcams...
In this work we present a general framework for robust error estimation in face recognition. The proposed formulation allows the simultaneous use of various loss functions for modeling the residual in face images, which usually follows non-standard distributions, depending on the image capturing conditions. Our method extends the current vast literature offering flexibility in the selection of the...
We address the problem of makeup face recognition. Our main idea is to incorporate different levels of features into a joint optimization framework. Specifically, we combine both mid-level (e.g. attributes) and low-level features to obtain a new representation for a better matching between makeup and non-makeup faces. Previous studies have discovered the influence of cosmetics on face recognition,...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Positional binding specifies feature positions for an image (or for text). We show how to incorporate position into a fully distributed vector formed from Vector Quantization, or add position to a vector formed from a Vector Symbolic Architecture. The method guarantees that small shifts in position result in small changes to the representation vector, and does not require an increase in vector size...
At the last decades, face analysis remains a challenging research topic in the computer vision area. Beyond the visible band, infrared images had shown several advantages for face detection and recognition. From the proposed approaches for analyzing these images, the local analysis is recognized by its feasibility to overcome typical undesirable conditions such as noise, illumination, and affine transformations...
In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local...
This paper explores modifications to a feedforward five-layer spiking convolutional network (SCN) of the ventral visual stream [Masquelier, T., Thorpe, S., Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Computational Biology, 3(2), 247–257]. The original model showed that a spike-timing-dependent plasticity (STDP) learning algorithm embedded in an appropriately...
Robust sparse representation has been applied to tackle some challenging problems in face recognition. In this paper, we propose a new method called occlusion pattern based sparse representation classification (OPSRC). First we find the contiguous occlusion area in the query image to create an occlusion pattern. Then, we add the occlusion pattern to all face images in the face image dictionary, resulting...
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