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AIIA Lab has developed a series of techniques for face representation in order to be used in facial biometrics and in particular in face verification problems, which are the main are of the lab's expertise. A series of techniques for exploiting the discriminant information in partbased face decomposition has been developed. The idea of these decompositions is to represent facial images as a linear combination of basis images that are intuitive related to facial parts based on Nonnegative Matrix Factorization (NMF). The NMF algorithm, like Principal Component Analysis (PCA), represents a facial image as a linear combination of basis images. The difference with PCA is that it does not allow negative elements either in the basis vectors or in the representation weights used in the linear combination of the basis images. This constraint results to radically different bases than PCA. On one hand, the bases of PCA are the Eigenfaces, some of which resemble distorted versions of the entire face. On the other hand, the bases of NMF are localized features that correspond better to the intuitive notion of face parts. Two classes of techniques have been developed:
The experiments have been conducted in ‘Configuration 1' of XM2VTS database as well. The tested methods are separated to those that produce local, partbased bases like NMF, DNMF and CSDNMF and to those whose bases are distorted versions of faces like Eigenfaces, Fisherfaces and NMFfaces. The best EER for the part based decompositions like NMF, LNMF, CSDNMF and DNMF was 3.0% in terms of EER and has been achieved by CSDNMF. The best EER for the Fisherfaces, Eigenfaces and NMFfaces has been 0.8% and has been achieved by the NMFfaces. The advantage of the subspace methods like NMFface etc is that they are very fast (less that 1m sec for a matching) but require perfect alignment of the training and the test images in order to perform well. T he proposed partbased discriminant techniques outperform other partbased techniques like NMF, LNMF etc., while the proposed NMFfaces outperform the well known Eigenfaces and Fisherfaces.

S. Zafeiriou, A. Tefas, I. Buciu and I. Pitas, "Exploiting Discriminant Information in Nonnegative Matrix Factorization with application to Frontal Face Verification", IEEE Transactions on Neural Networks, vol. 17, no. 3, pp. 683695, May, 2006. S. Zafeiriou, A. Tefas, I. Buciu and I. Pitas, "ClassSpecific Discriminant Nonnegative Matrix Factorization for Frontal Face Verification", in Proc. of Int. Conf. on Advances in Pattern Recognition (ICAPR 2005), Bath, United Kingdom, 2225 August, 2005. S. Zafeiriou, A. Tefas and I. Pitas, "Discriminant NMFfaces for Frontal Face Verification", in Proc. of IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP 2005), Mystic, Connecticut, 2830 September, 2005.
BioSec  Biometrics and Security, IST  IP, FP6 BioSecure  Biometrics for Secure Authentication, NOE, ISTFP6, EC. 

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Automatic Face Verification using Elastic Graph Matching (EGM) 

Face verification the procedure of establishing the veracity of an identity claim using one or more face images. It is a very difficult problem that spans various disciplines like computer vision, pattern recognition, computational intelligence etc. A few major problems concerning face verification are:
AIIA lab has developed a series of techniques for boosting the performance of EGM for face verification. Discriminant Elastic Graph Matching using morphological features A series of variants of the Elastic Graph Matching (EGM) algorithms have been developed. In EGM, the reference object graph is created by overlaying a rectangular elastic sparse graph on the object image and calculating a Gabor wavelet bank response or the output of morphological dilationerosion at each graph node. The graph matching process is implemented by a stochastic optimization of a cost function which takes into account both jet similarities and node deformation. Recently we have proposed a generalized framework that uses discriminant techniques at all the phases of EGM for face verification, the socalled discriminant elastic graph matching (DEGM) algorithm. The general framework of DEGM that was developed in order to enhance the face verification performance of an arbitrary elastic graph matching algorithm can be summarized in the following three steps:
In our DEGM we have used a novel morphological multiscale analysis that is more robust against noise and illumination changes than the typical morphological multiscale analysis (we call it Discriminant Normalized Morphological Elastic Graph Matching (DNMEGM)). The XM2VTS database has been used in our experiments since it is the state of the art database for comparing face verification technologies. The XM2VTS database contains 295 subjects, 4 recording sessions and two shots (repetitions) per recording session. The data is divided in three different sets: the training set, the evaluation set and the test set. The training set is used to create client and impostor models for each person. The evaluation set is used to learn the discriminant weights and the thresholds. In order to evaluate the DNMEGM algorithm the ‘Configuration I' experimental setup of the XM2VTS database was used and we have achieved an Equal Error Rate (EER) equal to 1.4% using fully automatic alignment. Unlike the most subspace techniques that require perfect alignment to perform well, EGM algorithm can be combined with fully automatic ones. The drawback of the EGM algorithm is the computational complexity for building the needed multiscale analysis and matching. For example at a resolution of 720x576 the algorithm takes 7 seconds for feature extraction and matching. The abbreviations of the tested techniques are shown in Table 1. The error rates according to the XM2VTS protocol are shown in Table 2. In Table 3 and 4 the performance of the proposed method in comparison to other methods tested in fully automatic manner is shown. As it can be verified the proposed technique outperforms all the other approaches in Configuration I and II of the XM2VTS database.
Discriminant Graph Structure for Face Verification A novel algorithm for finding discriminant personspecific facial models is proposed and tested for frontal face verification. The most discriminant features of a person's face are found and a deformable model is placed in the spatial coordinates that correspond to these discriminant features. The discriminant deformable models, for verifying the person's identity, that are learned through this procedure are elastic graphs that are dense in the facial areas considered discriminant for a specific person and sparse in other less significant facial areas. In order to find such graphs, we have introduced a heuristic cost optimization algorithm, which has as outcome the graph that optimizes a preselected discriminant cost. The cost is formed by calculating the significance of each node using discriminant values like the ones proposed. We assume that nodes with high discriminant values correspond to facial points with high discriminant capability. Ten, we try to represent, in a better way, the corresponding neighbourhood by adding more nodes round the original one. This practically means that we expand the nodes that are considered to be discriminant.
The proposed graphs have been applied in face verification in XM2VTS database and the results have been very close to the ones derived from DNMEGM (i.e. a TER =2.814 %) without using two all the discriminant steps of DNMEGM.

C.Kotropoulos, A.Tefas and I.Pitas, "Morphological elastic graph matching applied to frontal face authentication under wellcontrolled and real conditions", Pattern Recognition, vol. 33, no. 12, pp. 19351947, October, 2000. C.Kotropoulos,A.Tefas and I.Pitas, "Frontal face authentication using discriminating grids with morphological feature vectors", IEEE Transactions on Multimedia, vol. 2, no. 1, pp. 1426, March, 2000. C.Kotropoulos, A.Tefas and I.Pitas, "Frontal face authentication using morphological elastic graph matching", IEEE Transactions on Image Processing, vol. 9, no. 4, pp. 555560, April, 2000. A.Tefas, C.Kotropoulos and I.Pitas, "Using Support vector machines to enhance the performance of elastic graph matching for frontal face authentication", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 7, pp. 735746, July, 2001. A.Tefas, C.Kotropoulos and I.Pitas, "Face verification using elastic graph matching based on morphological signal decomposition", Signal Processing, vol. 82, no. 6, pp. 833851, June, 2002. S. Zafeiriou, A. Tefas and I. Pitas, "Elastic Graph Matching versus Linear Subspace Methods for Frontal Face Verification", in Proc. of IEEE Int. Workshop on Nonlinear Signal and Image Processing (NSIP 2005), Sapporo, Japan, 1820 May, 2005.
BioSec  Biometrics and Security, IST  IP, FP6 BioSecure  Biometrics for Secure Authentication, NOE, ISTFP6, EC. 

© 2006 

Thermal video and image processing is a fundamental component of an advanced mobile service that will provide critical multimodal communication support for emergency teams during rescue operations. After the introduction of the new service, rescue operations will benefit enormously from sophisticated multimodal interaction and online, onsite access to data services providing uptodate operation status information, as well as details concerning aspects of the emergency, such as location and environment. Thermal camera documents are pretty reliable and accurate regarding showing the exact temperature values in the scene. As these sensors have recently started to spread, few image processing analysis performed on the data they provide.
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A. Hajdu, A. Roubies and I. Pitas, ”Optimised chamfer matching for snakebased image contour representations”, in IEEE International Conference on Multimedia & Expo (ICME2006), Toronto, Canada, 2006. A. Roubies, A. Hajdu and I. Pitas, "Improving Concavity Performance of Snake Algorithms", in Proc. of Int. Symposium Communications, Control and Signal Processing (ISCCSP 2006), Marrakech, Morocco, 1315 March, 2006.
SHARE  “Mobile Support for Rescue Forces, Integrating Multiple Modes of Interaction” (FP6 004218) 

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Mosaic Images
Outcome of face detection
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K. Sobottka and I. Pitas, "A Fully Automatic Approach to Facial Feature Detection and Tracking", in Proc. of 1st Int. Conf. on Audio and Videobased Biometric Person Authentication (AVBPA'97), pp. 7784, CransMontana, Switzerland, 1214 March, 1997. K.Sobottka and I.Pitas, "A novel method for automatic face segmentation, facial feature extraction and tracking", Image Communication, Elsevier , vol. 12, no. 3, pp. 263281, June, 1998. (pp. 118,pp. 1922, pp. 2334)
M2VTS  "Multimodal Verification Techniques for Teleservices and Security Applications" 

© 2006 

Our approaches MDLA Grid matching procedure
Discriminatory power coefficients for the grid nodes in MDLA
MSDMDLA Image Analysis
Grid matching procedure
Discriminatory power coefficients for the grid nodes in MDLA Morphological Pyramids Image Analysis
Grid matching procedure
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M2VTS  "Multimodal Verification Techniques for Teleservices and Security Applications" 

© 2006 

Samples images from MATRANORTEL database They have been recorded under variable lighting conditions. The faces appear in different sizes and at different positions. The subjects' facial expressions are not neutral.
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Robust Feature Coefficients with Applications to Automatic Speaker Recognition 

Presentation of feature extraction techniques The selection of the best parameter representation of acoustic data is an important task in designing a speaker recognition In these experiments we use two different kinds of signal representation: cepstral and melfrequency cepstral coefficients. The experiments
The sequence of processing in order to extract the cepstral coefficients is showed in the block diagram
The sequence of processing in order to extract the melfrequency cepstral features includes the following steps:
Performance Results In order to estimate the performance of the feature extraction techniques the experimental data showed in the table was used:
During the experiments, two different kinds of classification error rates have been measured. The first kind is referred to the percentage of the identification error rate in a closedset of speakers. The table below, shows the exact percentage of the identification error in 8 different shot combinations.
The second kind of recognition error is referred to the False Acceptance (FA) and the False Rejection (FR) Rate in an openset of speakers based on the Brussels protocol training and testing procedures. For both cepstral and melcepstral parameters the Receiver Operating Characteristics (ROC) are plotted in figures I and II.
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K. Sobottka and I. Pitas, "A Fully Automatic Approach to Facial Feature Detection and Tracking", in Proc. of 1st Int. Conf. on Audio and Videobased Biometric Person Authentication (AVBPA'97), pp. 7784, CransMontana, Switzerland, 1214 March, 1997. K.Sobottka and I.Pitas, "A novel method for automatic face segmentation, facial feature extraction and tracking", Image Communication, Elsevier , vol. 12, no. 3, pp. 263281, June, 1998. (pp. 118,pp. 1922, pp. 2334)
M2VTS  "Multimodal Verification Techniques for Teleservices and Security Applications" 

© 2006 
