Hichem Sahbi

Face detection using coarse-to-fine support vector classifiers

We describe a new face detection algorithm based on a hierarchy of support vector classifiers (SVM) designed for efficient computation. The hierarchy serves as a platform for a coarse-to-fine search for faces: most of the image is quickly rejected as background and the processing naturally concentrates on regions containing faces and face-like structures. The hierarchy is tree-structured: In proceeding from the root to the leaves, the SVM gradually increase in complexity (measured by the number of support vectors) and discrimination (measured by the false alarm rate), but decrease in the level of invariance. Reduced complexity is achieved by clustering support vectors and shifting the decision boundary in order to satisfy a conservation hypothesis that preserves positive responses from the original set of support vectors. The computation is organized as a depth-first search and cancel strategy. The gain in efficiency is enormous.

Robust Face Recognition Using Dynamic Space Warping

The utility of face recognition for multimedia indexing is enhanced by using accurate detection and alignment of salient invariant face features. The face recognition can be performed using template matching or a feature-based-approach, but both these methods suffer from occlusion and require an a priori model for extracting information. To avoid these drawbacks, we present in this paper a complete scheme for face recognition based on salient feature extraction in challenging conditions, which is performed without an a priori or learned model. These features are used in a matching process that overcomes occlusion effects and facial expressions using the dynamic space warping which aligns each feature in the query image, if possible, with its corresponding feature in the gallery set. Thus, we make face recognition robust to low frequency variations (like the presence of occlusion, etc) as well as to high frequency variations (like expression, gender, etc). A maximum likelihood scheme is used to make the recognition process more precise, as is shown in the experiments.

From coarse to fine skin and face detection

A method for fine skin and face detection is described that starts from a coarse color segmentation. Some regions represents parts of human skin and are selection by minimizing an error between the color distribution of each region and the output of a compression decompression neural network, which learns skin color distribution for several populations of different ethnicity. This ANN is used to find a collection of skin regions, which is used in a second learning step to provide parameters for a Gaussian mixture model. A finer classification is performed using a Bayesian framework and makes the skin and face detection invariant to scale and lighting conditions. Finally, a face shape based model is used to decide whether a skin region is a face or not.

Coarse to Fine Face Detection Based on Skin Color Adaption

In this paper we present a skin color approach for fast and accurate face detection which combines skin color learning and image segmentation. This approach starts from a coarse segmentation which provides regions of homogeneous statistical color distribution. Some regions represent parts of human skin and are selected by minimizing an error between the color distribution of each region and the output of a compression decompression neural network, which learns skin color distribution for several populations of different ethnicity. This ANN is used to find a collection of skin regions which are used to estimate the new parameters of the Gaussian models using a 2-means fuzzy clustering in order to adapt these parameters to the context of the input image. A Bayesian framework is used to perform a finer classification and makes the skin and face detection process invariant to scale and lighting conditions. Finally, a face shape based model is used to validate or not the face hypothesis on each skin region.

Coarse-to-fine support vector classifiers for face detection

We describe a new hierarchical face detection algorithm which allows fast background rejection in major parts of images and fine processing in area containing faces. This coarse-to-fine classification strategy is based on learning support vector classifiers (SVMs) with increasing evaluation complexity (resp. decreasing invariance and false alarm rates) top-down in the hierarchy. The complexity, in terms of the number of support vectors, of each detector in the hierarchy is reduced by clustering. We introduce the bias variation technique which allows each simplified SVM function to satisfy the conservation hypothesis as a criterion to get a consistent classifier in terms of detection rate, false alarms and background rejection efficiency. Face detection is performed using a depth-first search and cancel strategy which, for a given face pattern, finds a root-leaf path with a sequence of positive answers.

Applying interest operators in semi-fragile video watermarking

In this article we present a semi-fragile watermarking scheme for authenticating intra-coded frames in compressed digital videos. The scheme provides the detection of content-changing manipulations while being moderately robust against content-preserving manipulations. We describe a watermarking method based on invariant features referred to as interest points. The features are extracted using the Moravec-Operator. Out of the interest points we generate a binary feature mask, which is embedded robustly as watermark into the video. In the verification process we compare the detected watermark with the interest points from the video to be verified. We present test results evaluating the robustness against content-preserving manipulations and the fragility in terms of content-changing manipulations. Beside the discussion of the results we propose a procedure to provide security of the scheme against forgery attacks.

Fuzzy Clustering: Consistency of Entropy Regularization

We introduce in this paper a new formulation of the regularized fuzzyc-means (FCM) algorithm which allows us to set automatic ally the actual number of clusters. The approach is based on the minimization of an objective function which mixes, via a particular parameter, a classic al FCM term and an entropy regularizer. The method uses a new exponential form of the fuzzy memberships which ensures the consistency of their bounds and makes it possible to interpret the mixing parameter as the variance (or scale) of the clusters. This variance closely related to the number of clusters, provides us with a more intuitive and an easy to set parameter.

Validity of Fuzzy Clustering Using Entropy Regularization

We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which allows us to find automatically the actual number of clusters. The approach is based on the minimization of an objective function which mixes, via a particular parameter, a classical FCM term and a new entropy regularizer. The main contribution of the method is the introduction of a new exponential form of the fuzzy memberships which ensures the consistency of their bounds and makes it possible to interpret the mixing parameter as the variance (or scale) of the clusters. This variance closely related to the number of clusters, provides us with an intuitive and an easy to set parameter. We will discuss the proposed approach from the regularization point-of-view and we will demonstrate its validity both analytically and experimentally. We will show an extension of the method to nonlinearly separable data. Finally, we will illustrate preliminary results both on simple toy examples as well as database categorization problems

Automatic textual annotation of video news based on semantic visual object extraction

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the MediaWorks project that consists on an hybrid text-image indexing and retrieval plateform for video news.

Network-Dependent Image Annotation Based on Explicit Context-Dependent Kernel Maps

It is commonly known that the success of support vector machines in image classification and annotation it highly dependent on the relevance of the chosen kernels. The latter, defined as symmetric positive semi-definite functions, take high values when images share similar visual content and vice-versa. However, usual kernels relying only on the visual content are not appropriate in order to capture the true semantics of images which are nowadays expressed through the rich contextual cues available in image collections. Relevant kernels should instead reserve high values not only when images share similar content but also similar context. In this paper, we introduce a novel method that upgrades usual kernels and makes them context-dependent. Our kernel solution corresponds to an optimum of an energy function that trades off content and context. We will show that the proposed kernel can be expressed with an explicit mapping which is computationally efficient and also effective for image annotation. We corroborate all these statements through our participation in the recent and challenging Image CLEF 2013 annotation benchmark which ranks our method first among 58 participants runs.