Publications by Enver Sangineto

In this paper we propose an integrated system for face detection and face recognition based on improved versions of state-of-the-art statistical learning techniques such as Boosting and LDA. Both the detection and the recognition processes are performed on facial features (e.g., the eyes, the nose, the mouth, etc) in order to improve the recognition accuracy and to exploit their statistical independence in the training phase. Experimental results on real images show the superiority of our proposed techniques with respect to the existing ones in both the detection and the recognition phase. © 2009 Springer Berlin Heidelberg.

In this paper we present a face recognition system based on the Scale Invariant Feature Transform (SIFT) image descriptors recently proposed by Lowe [6] and largely used in generic object recognition tasks. We show how SIFT descriptors can be used in a robust face recognition system coupled with some simple image normalization processes and geometric constraints on SIFT matchings. In the paper we present an extensive experimental evaluation of the prosed SIFT-based face recognition approach comparing it with a “standard” Linear Discriminant Analysis (LDA)-based method, commonly considered one of the best performing face recognition technique. The two systems have been tested using images collected from different face recognition benchmarks in order to simulate real-life applications in which image acquisition parameters largely vary from one query image to the other. In all our tests the SIFT-based method clearly outperformed the LDA-based one, showing that the a priori knowledge embedded in the SIFT local description of image appearances is more robust than trainingbased systems in which appearance variability factors need to be off-line learned. © 2008 Taru Publication.

We present in this paper an elephant photo identification system based on the shape comparison of the nicks characterizing the elephants' ears. The method we propose can deal with very cluttered and noisy images as the ones commonly used by zoologists for wild elephant photo identification. Difficult segmentation problems are solved using rough position information input by the user. Such information is used by the system as a basis for a set of segmentation and normalization hypotheses aiming at comparing a query photo Q with different photos of the system's database possibly representing the same individual as Q. The proposed shape comparison method, based on matching multiple, non-connected curves, can be applied to different retrieval by shape problems. Examples with real wild elephant photos are shown. (C) 2007 Elsevier Ltd. All rights reserved.