We propose an approach for combining the outputs of multiple neural network classifiers to reach a unified decision with improved performance in terms of higher recognition/classification rates. Our architecture consists of an ensemble of connectionist networks-radial basis functions (RBF)-and inductive decision trees (DT). The specific characteristics of our architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, (b) categorical classifications using decision trees, (c) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds. Experiments proving the feasibility of our architecture were performed on face recognition using 900 images, gender and ethnic classification tasks using 3000 images from the FERET facial database. Specifically, we observe that a small number of networks (two or three) were sufficient for yielding a much improved classification rate as opposed to using a single RBF network or an ensemble of RBF networks employing a voting scheme.

In this paper we describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The FERET database allows us to demonstrate performance on hundreds or thousands of images. The mixture of experts is implemented using the "divide and conquer" modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the "gating network" components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel ("expert") for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification.

This paper considers hybrid classification architectures for gender and ethnic classification of human faces and shows their feasibility using a collection of 3006 face images corresponding to 1009 subjects from the FERET database. The hybrid approach consists of an ensemble of RBF networks and inductive decision trees (DT). Experimental cross validation (CV) results yield an average accuracy rate of (a) 96% on the gender classification task and (b) 94% on the ethnic classification task. The benefits of our hybrid architecture include: (i) robustness via query by consensus provided by the ensembles of RBF networks, and (ii) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds provided by using only DT.

Most of the research on face recognition addresses the MATCH problem and it assumes a closed universe where there is no need for a REJECT ('false positive') option. The SURVEILLANCE problem is addressed indirectly, if at all, through the MATCH problem, where the size of the gallery rather than that of the probe set is very large. This paper addresses the proper surveillance problem where the size of the probe ('unknown image') set vs. gallery ('known image') set is 450 vs. 50 frontal images. We developed robust face ID verification ('classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET face data base. The hybrid classifier architecture consists of an ensemble of connectionist networks-Radial Basis Functions (RBF) and inductive decision trees (DT). Experimental results prove the feasibility of our approach and yield 97% accuracy using the probe and gallery sets specified above.

Hand gestures are the natural form of communication among people, yet human-computer interaction is still limited to mice movements. The use of hand gestures in the field of human-computer interaction has attracted renewed interest in the past several years. Special glove-based devices have been developed to analyze finger and hand motion and use them to manipulate and explore virtual worlds. To further enrich the naturalness of the interaction, different computer vision-based techniques have been developed. At the same time the need for more efficient systems has resulted in new gesture recognition approaches. In this paper we present an hybrid intelligent system for hand gesture recognition. The hybrid approach consists of an ensemble of connectionist networks — radial basis functions (RBF) — and inductive decision trees (AQDT). Cross Validation (CV) experimental results yield a false negative rate of 1.7% and a false positive rate of 1% while the evaluation takes place on a data base including 150 images corresponding to 15 gestures of 5 subjects. In order to assess the robustness of the system, the vocabulary of the gestures has been increased from 15 to 25 and the size of the database from 150 to 750 images corresponding now to 15 subjects. Cross Validation (CV) experimental results yield a false negative rate of 3.6% and a false positive rate of 1.8% respectively. The benefits of our hybrid architecture include (i) robustness via query by consensus as provided by ensembles of networks when facing the inherent variability of the image formation and data acquisition process, (ii) classifications made using decision trees, (iii) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds and (iv) interpretability of the way classification and retrieval is eventually achieved.

This paper advances the methodology of hybrid classification architectures for face and hand gesture recognition tasks and shows their feasibility through experimental studies using the FERET data base and gesture images. The hybrid architecture, consisting of an ensemble of connectionist networks-radial basis functions (RBF)-and inductive decision trees (DT), combines the merits of 'holistic' template matching with those of 'abstractive' matching using discrete features and subject to both positive and negative learning. The hybrid architecture, quite general as it applies to both face and hand gesture recognition, derives its robustness from (i) consensus using ensembles of RBF network;, and (ii) flexible matching using categorical classification via decision trees. The experimental results, proving the feasibility of our approach, yield (i) 93% accuracy, using cross validation, for contents-based image retrieval (CBIR) subject to correct ID matching tasks, such as 'find Joe Smith with/without glasses', on a data bate of 200 images, and (ii) 96% accuracy using cross validation, for forensic verification on a data base consisting of 102 images corresponding to 350 subjects (of whom 102 are duplicates). Cross validation results on the hand gesture recognition task yield a false negative rate of 3.6% and a false positive rate of 1.8%, using a data base of 750 images corresponding to 25 hand gestures.

The paper proposes a novel algorithm for face detection using decision trees (DT) and shows its generality and feasibility using a database consisting of 2340 face images from the FERET database (corresponding to 817 subjects and including 190 sets of duplicates) over a semi-uniform background. The approach used for face detection involves three main stages, those of location, cropping, and post-processing. The first stage finds a rough approximation for the possible location of the face box, the second stage will refine it, and the last stage decider whether a face is present in the image and if the answer is positive would normalize the face image. The algorithm does not require multiple (scale) templates and the accuracy achieved is 96%. Accuracy is based on the visual observation that the face box includes both eyes, nose, and mouth, and that the top side of the box is below the hairline. Experiments were also performed to assess the accuracy of the algorithm in rejecting images where no face is present. Using a small database of 25 images of various but complex backgrounds the algorithm failed on two images for an overall accuracy rate of 92%.

We describe a novel approach for fully automated face recognition and show its feasibility on a large database of facial images (FERET). Our approach, based on a hybrid architecture consisting of an ensemble of radial basis function (RBF) neural networks and inductive decision trees, combines the merits of "abstractive" features with those of "holistic" template matching. The benefits of our architecture include: 1) robust detection of facial landmarks using decision trees, and 2) robust face recognition using consensus methods over ensembles of RBF networks. Experiments carried out using k-fold cross validation on a large database consisting of 748 images corresponding to 374 subjects, among them 11 duplicates, yield on the average 87% correct match, and 99% correct surveillance ("verification").