Augusto Destrero

A Sparsity-Enforcing Method for Learning Face Features

In this paper, we propose a new trainable system for selecting face features from over-complete dictionaries of image measurements. The starting point is an iterative thresholding algorithm which provides sparse solutions to linear systems of equations. Although the proposed methodology is quite general and could be applied to various image classification tasks, we focus here on the case study of face and eyes detection. For our initial representation, we adopt rectangular features in order to allow straightforward comparisons with existing techniques. For computational efficiency and memory saving requirements, instead of implementing the full optimization scheme on tenths of thousands of features, we propose a three-stage architecture which consists of finding first intermediate solutions to smaller size optimization problems, then merging the obtained results, and next applying further selection procedures. The devised system requires the solution of a number of independent problems, and, hence, the necessary computations could be implemented in parallel. Experimental results obtained on both benchmark and newly acquired face and eyes images indicate that our method is a serious competitor to other feature selection schemes recently popularized in computer vision for dealing with problems of real-time object detection. A major advantage of the proposed system is that it performs well even with relatively small training sets.

Feature selection for high-dimensional data

This paper focuses on feature selection for problems dealing with high-dimensional data. We discuss the benefits of adopting a regularized approach with L1 or L1–L2 penalties in two different applications—microarray data analysis in computational biology and object detection in computer vision. We describe general algorithmic aspects as well as architecture issues specific to the two domains. The very promising results obtained show how the proposed approach can be useful in quite different fields of application.

A Regularized Framework for Feature Selection in Face Detection and Authentication

This paper proposes a general framework for selecting features in the computer vision domain—i.e., learning descriptions from data—where the prior knowledge related to the application is confined in the early stages. The main building block is a regularization algorithm based on a penalty term enforcing sparsity. The overall strategy we propose is also effective for training sets of limited size and reaches competitive performances with respect to the state-of-the-art. To show the versatility of the proposed strategy we apply it to both face detection and authentication, implementing two modules of a monitoring system working in real time in our lab. Aside from the choices of the feature dictionary and the training data, which require prior knowledge on the problem, the proposed method is fully automatic. The very good results obtained in different applications speak for the generality and the robustness of the framework.

A regularized approach to feature selection for face detection

In this paper we present a trainable method for selecting features from an overcomplete dictionary of measurements. The starting point is a thresholded version of the Landweber algorithm for providing a sparse solution to a linear system of equations. We consider the problem of face detection and adopt rectangular features as an initial representation for allowing straightforward comparisons with existing techniques. For computational efficiency and memory requirements, instead of implementing the full optimization scheme on tenths of thousands of features, we propose to first solve a number of smaller size optimization problems obtained by randomly sub-sampling the feature vector, and then recombining the selected features. The obtained set is still highly redundant, so we further apply feature selection. The final feature selection system is an efficient two-stages architecture. Experimental results of an optimized version of the method on face images and image sequences indicate that this method is a serious competitor of other feature selection schemes recently popularized in computer vision for dealing with problems of real time object detection.

Combined Motion and Appearance Models for Robust Object Tracking in Real-Time

This paper proposes a tracking architecture that finds a trade-off between accuracy and efficiency, via a combined solution of motion and appearance information. We explore the use of color features into a tracking pipeline based on Kalman filtering. The devised architecture is made of simple modules, combined to reach a robust final result, while keeping the computation cost low (we perform

A Classification Architecture Based on Connected Components for Text Detection in Unconstrained Environments

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A system for face detection and tracking in unconstrained environments

fps). The method has been evaluated on three benchmark datasets and is currently under use on real video-surveillance systems, reporting very good tracking results.

A trainable system for face detection in unconstrained environments

The paper presents a method for efficient text detection in unconstrained environments, based on image featuresderived from connected components and on a classification architecture implementing a focus of attention approach.The main application motivating the work is container code detection with the final goal ofchecking freight trains composition. Although the method is strongly influenced by the applicationexperimental evidence speaks in favour of its generality: we present results on container codes, car plates images andon the benchmark dataset ICDAR.

Low-cost face biometry for visually impaired users

We describe a trainable system for face detection and tracking. The structure of the system is based on multiple cues that discard non face areas as soon as possible: we combine motion, skin, and face detection. The latter is the core of our system and consists of a hierarchy of small SVM classifiers built on the output of an automatic feature selection procedure. Our feature selection is entirely data-driven and allows us to obtain powerful descriptions from a relatively small set of data. Finally, a Kalman tracking on the face region optimizes detection results over time. We present an experimental analysis of the face detection module and results obtained with the whole system on the specific task of counting people entering the scene.

A prototype system for unconstrained face verication based on statistical learning

This paper describes a monitoring system that implements real-time face detection. The structure of the system is based on multiple cues that discard non face areas as soon as possible: we combine motion, skin, and face detection. The latter is the core of our system and consists of a hierarchy of small SVM classifiers built on the output of a feature selection procedure. Following face detection, a Kalman tracking on the face region allows us to optimize results over time. We present an experimental analysis of the face detection module and results obtained with the whole system on the specific task of counting people entering the scene.