An efficient face verification method in a transformed domain

Faundez-Zanuy, Marcos; Roure, Josep; Espinosa-Duro, V.; Ramírez, Juan Antonio Ortega

doi:10.1016/j.patrec.2006.12.005

Cited by 28 publications

(18 citation statements)

References 6 publications

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“…Thus, for instance, it is possible to enhance a face recognition system [18] by means of data fusion between biometric classifiers performing on each kind of image [19]. In fact, experimental results of Table 2 reveal that the mutual information is low, between 0.89 and 1.55 bit when comparing a pair of images of the same person acquired with a different kinds of sensor and between 1.58 and 2.10 bit when acquired with same kind of sensor.…”

Section: Discussionmentioning

confidence: 99%

Beyond Cognitive Signals

Espinosa-Duro¹,

Faundez-Zanuy²,

Mekyska

2010

Cogn Comput

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Although audio-visual human systems have several well-known limitations, artificial sensors can measure information beyond our limits. What would happen if we were able to overcome our limitations? Would we be able to obtain a better knowledge of our environment? Or the information beyond our limits is redundant? In this paper, we compare infrared, thermal and visible images from an information theory point of view. We have acquired a small database and compared several measurements over these images. While infrasounds and ultrasounds are not directly applicable, for instance, to speaker recognition due to the impossibility of human beings generating sounds in these frequencies, this is not the case with image signals beyond the visible spectrum for face recognition. We have observed that visible, near-infrared and thermal images contain a small amount of redundancy (less than 1,55 bits).

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Section: Discussionmentioning

confidence: 99%

Beyond Cognitive Signals

Espinosa-Duro¹,

Faundez-Zanuy²,

Mekyska

2010

Cogn Comput

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“…They have the following main features: Figure 4 shows some snapshots of these three databases. Feature extraction from face images is based on Discrete Cosine Transform (DCT) and can be found in our previous work [16] and [17]. Tables 1 and 2 present the experimental results with different training and testing conditions.…”

Section: Experiments With Face Databasesmentioning

confidence: 99%

Biometric Recognition Performing in a Bioinspired System

Fabregas

Faundez-Zanuy

2009

Cogn Comput

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In this study, we propose a set of biometric recognition experiments in similar conditions to real operating systems. This implies a jump from the usual laboratory conditions to a more real situation where the amount of variability between training and testing samples is large. We present experiments with face, hand-geometry, and signature recognition training a ''universal classifier'' able to decide if two input samples belong to the same person or not. During test, we recognize samples of a different database not used during classifier training. Training with the ORL face database and testing with the AR database provides a 5.1% error rate in verification operation, while training and testing with the same database yields 2.5%. For hand-geometry databases, we obtain 4.33 and 0.16% for different and same testing and training databases, respectively. For signature recognition, we obtain 1.36 and 4.14% for different and same testing and training databases, respectively. Our proposed system implies a very low computational cost to introduce/remove a user in the database, which is a crucial point for a real operation biometric system.

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“…The core of this system is a hewlett-packard notebook with touch screen (suitable for online signature acquisition). The technological solutions behind each biometric trait are DCT-NN [11] for face recognition, SVM for hand-geometry, HMM for signature and GMM for speaker recognition. Figure 6 shows some snapshots of the screen and figure 7 shows a physical installation in a wall for door opening system.…”

Section: Proposed Approach (Step 2)mentioning

confidence: 99%

Biometric Database Acquisition Close to “Real World” Conditions

Faundez-Zanuy¹,

Fabregas²,

Ferrer-Ballester

et al. 2010

Development of Multimodal Interfaces: Active Listening and Synchrony

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Biometric database acquisition close to "real world" conditionsMarcos Faundez-Zanuy (1), Joan Fàbregas (1), Miguel Ángel Ferrer-Ballester (2), Aythami Morales (2), Javier Ortega-Garcia (3), Guillermo Gonzalez de Rivera (3), Javier Garrido (3) (1) Escola Universitària Politècnica de Mataró (Adscrita a la UPC), (2) GPDS Universidad de Las Palmas de Gran Canaria (3) ATVS, Universidad Autónoma de Madrid faundez@eupmt.es http://www.gpds.ulpgc.es/biopassweb/biopass.htm Abstract. In this paper we present an autonomous biometric device developed in the framework of a national project. This system is able to capture speech, hand-geometry, online signature and face, and can open a door when the user is positively verified. Nevertheless the main purpose is to acquire a database without supervision (normal databases are collected in the presence of a supervisor that tells you what to do in front of the device, which is an unrealistic situation). This system will permit us to explain the main differences between what we call "real conditions" as opposed to "laboratory conditions".

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An efficient face verification method in a transformed domain

Cited by 28 publications

References 6 publications

Beyond Cognitive Signals

Beyond Cognitive Signals

Biometric Recognition Performing in a Bioinspired System

Biometric Database Acquisition Close to “Real World” Conditions

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