Khalid Idrissi

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The aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015 IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised,(More)
Facial expression’s machine analysis is one of the most challenging problems in Human–Computer Interaction (HCI). Naturally, facial expressions depend on subtle movements of facial muscles to show emotional states. After having studied the relations between basic expressions and corresponding facial deformation models, we propose two new textons, VTB and(More)
We propose an efficient linear similarity metric learning method for face verification called Triangular Similarity Metric Learning (TSML). Compared with relevant state-of-the-art work, this method improves the efficiency of learning the cosine similarity while keeping effectiveness. Concretely, we present a geometrical interpretation based on the triangle(More)
In this paper we address the problem of generative object categorization in computer vision. We propose a Bayesian model using Hierarchical Dirichlet Processes mixing AdaBoost learning. Although previous methods trained HDP model for one or two latent themes, our proposed approach uses small-patch-independent-words of appearance-based descriptor and shape(More)
In this paper, we develop an automatic facial expression recognition system which establishes relations between facial expressions and the facial parts changes. Here, the differences between neutral and emotional states are used to help locating and identifying the essential facial parts for human expressions. For face description, region-based method to(More)
This paper presents a framework using siamese Multi-layer Perceptrons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical(More)
We propose a novel, interactive content-aware zooming operator that allows effective and efficient visualization of high resolution images on small screens, which may have different aspect ratios compared to the input images. Our approach applies an image retargeting method in order to fit an entire image into the limited screen space. This can provide(More)
This paper presents a new method for similarity metric learning, called Logistic Similarity Metric Learning (LSML), where the cost is formulated as the logistic loss function, which gives a probability estimation of a pair of faces being similar. Especially, we propose to shift the similarity decision boundary gaining significant performance improvement. We(More)
High-dimensional indexing methods have been proved quite useful for response time improvement. Based on Euclidian distance, many of them have been proposed for applications where data vectors are high-dimensional. However, these methods do not generally support efficiently similarity search when dealing with heterogeneous data vectors. In this paper, we(More)