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Dive into the research topics where Mauricio Correa is active.

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Featured researches published by Mauricio Correa.


EURASIP Journal on Advances in Signal Processing | 2009

Recognition of faces in unconstrained environments: a comparative study

Javier Ruiz-del-Solar; Rodrigo Verschae; Mauricio Correa

The aim of this work is to carry out a comparative study of face recognition methods that are suitable to work in unconstrained environments. The analyzed methods are selected by considering their performance in former comparative studies, in addition to be real-time, to require just one image per person, and to be fully online. In the study two local-matching methods, histograms of LBP features and Gabor Jet descriptors, one holistic method, generalized PCA, and two image-matching methods, SIFT-based and ERCF-based, are analyzed. The methods are compared using the FERET, LFW, UCHFaceHRI, and FRGC databases, which allows evaluating them in real-world conditions that include variations in scale, pose, lighting, focus, resolution, facial expression, accessories, makeup, occlusions, background and photographic quality. Main conclusions of this study are: there is a large dependence of the methods on the amount of face and background information that is included in the faces images, and the performance of all methods decreases largely with outdoor-illumination. The analyzed methods are robust to inaccurate alignment, face occlusions, and variations in expressions, to a large degree. LBP-based methods are an excellent election if we need real-time operation as well as high recognition rates.


Pattern Recognition | 2012

A comparative study of thermal face recognition methods in unconstrained environments

Gabriel Hermosilla; Javier Ruiz-del-Solar; Rodrigo Verschae; Mauricio Correa

The recognition of faces in unconstrained environments is a challenging problem. The aim of this work is to carry out a comparative study of face recognition methods working in the thermal spectrum (8-12@mm) that are suitable for working properly in these environments. The analyzed methods were selected by considering their performance in former comparative studies, in addition to being real-time, to requiring just one image per person, and to being fully online (no requirements of offline enrollment). Thus, in this study three local-matching methods based on histograms of Local Binary Pattern (LBP) features, on histograms of Weber Linear Descriptors (WLD), and on Gabor Jet Descriptors (GJD), as well as two global image-matching method based on Scale-Invariant Feature Transform (SIFT) Descriptors, and Speeded Up Robust Features (SURF) Descriptors, are analyzed. The methods are compared using the Equinox and UCHThermalFace databases. The use of these databases allows evaluating the methods in real-world conditions that include natural variations in illumination, indoor/outdoor setup, facial expression, pose, accessories, occlusions, and background. The UCHThermalFace database is described for the first time in this article and WLD is used for the first time in face recognition. The results of this comparative study are intended to be a guide for developers of face recognition systems. The main conclusions of this study are: (i) all analyzed methods perform very well under the conditions in which they were evaluated, except for the case of GJD that has low performance in outdoor setups; (ii) the best tradeoff between high recognition rate and fast processing speed is obtained by WLD-based methods, although the highest recognition rate in all cases is obtained by SIFT-based methods; and (iii) in experiments where the test images are acquired in an outdoor setup and the gallery images are acquired in an indoor setup, or vice versa, the performance of all evaluated methods is very low. As part of the future work, the use of normalization algorithms and calibration procedures in order to tackle this last issue will be analyzed.


machine vision applications | 2008

A unified learning framework for object detection and classification using nested cascades of boosted classifiers

Rodrigo Verschae; Javier Ruiz-del-Solar; Mauricio Correa

In this paper a unified learning framework for object detection and classification using nested cascades of boosted classifiers is proposed. The most interesting aspect of this framework is the integration of powerful learning capabilities together with effective training procedures, which allows building detection and classification systems with high accuracy, robustness, processing speed, and training speed. The proposed framework allows us to build state of the art face detection, eyes detection, and gender classification systems. The performance of these systems is validated and analyzed using standard face databases (BioID, FERET and CMU-MIT), and a new face database (UCHFACE).


Journal of Intelligent and Robotic Systems | 2012

Human Detection and Identification by Robots Using Thermal and Visual Information in Domestic Environments

Mauricio Correa; Gabriel Hermosilla; Rodrigo Verschae; Javier Ruiz-del-Solar

In this paper a robust system for enabling robots to detect and identify humans in domestic environments is proposed. Robust human detection is achieved through the use of thermal and visual information sources that are integrated to detect human-candidate objects, which are further processed in order to verify the presence of humans and their identity using face information in the thermal and visual spectrums. Face detection is used to verify the presence of humans, and face recognition to identify them. Active vision mechanisms are employed in order to improve the relative pose of a candidate object/person in case direct identification is not possible. The response of the different modules is characterized, and the proposed system is validated using image databases of real domestic environments, and human detection and identification benchmarks of the RoboCup@Home research community.


iberoamerican congress on pattern recognition | 2006

Gender classification of faces using adaboost

Rodrigo Verschae; Javier Ruiz-del-Solar; Mauricio Correa

In this work it is described a framework for classifying face images using Adaboost and domain-partitioning based classifiers. The most interesting aspect of this framework is the capability of building classification systems with high accuracy in dynamical environments, which achieve, at the same time, high processing and training speed. We apply this framework to the specific problem of gender classification. We built several gender classification systems under the proposed framework using different features (LBP, wavelets, rectangular, etc.). These systems are analyzed and evaluated using standard face databases (FERET and BioID), and a new gender classification database of real-world images.


robot soccer world cup | 2010

Real-time hand gesture recognition for human robot interaction

Mauricio Correa; Javier Ruiz-del-Solar; Rodrigo Verschae; Jong Lee-Ferng; Nelson Castillo

In this article a hand gesture recognition system that allows interacting with a service robot, in dynamic environments and in real-time, is proposed. The system detects hands and static gestures using cascade of boosted classifiers, and recognize dynamic gestures by computing temporal statistics of the hand’s positions and velocities, and classifying these features using a Bayes classifier. The main novelty of the proposed approach is the use of context information to adapt continuously the skin model used in the detection of hand candidates, to restrict the image’s regions that need to be analyzed, and to cut down the number of scales that need to be considered in the hand-searching and gesture-recognition processes. The system performance is validated in real video sequences. In average the system recognized static gestures in 70% of the cases, dynamic gestures in 75% of them, and it runs at a variable speed of 5-10 frames per second.


latin american robotics symposium | 2009

Face recognition using thermal infrared images for Human-Robot Interaction applications: A comparative study

Gabriel Hermosilla; Javier Ruiz-del-Solar; Rodrigo Verschae; Mauricio Correa

The aim of this work is to carry out a comparative study of face-recognition methods for Human-Robot Interaction (HRI) applications using long wave infrared images. The analyzed methods are selected by considering their suitability for HRI use, and their performance in former comparative studies. The methods are compared using visible and infrared (8–12 µm) images. The results of this comparative study are intended to be a guide for developers of face recognition systems for HRI.


robot soccer world cup | 2010

Analyzing the human-robot interaction abilities of a general-purpose social robot in different naturalistic environments

Javier Ruiz-del-Solar; Mauricio Mascaró; Mauricio Correa; Fernando Bernuy; Romina Riquelme; Rodrigo Verschae

The main goal of this article is to report and analyze the applicability of a general-purpose social robot, developed in the context of the RoboCup @Home league, in three different naturalistic environments: (i) home, (ii) school classroom, and (iii) public space settings. The evaluation of the robot’s performance relies on its degree of social acceptance, and its abilities to express emotions and to interact with humans using human-like codes. The reported experiments show that the robot has a large acceptance from expert and non-expert human users, and that it is able to successfully interact with humans using human-like interaction mechanisms, such as speech and visual cues (particularly face information). It is remarkable that the robot can even teach children in a real classroom.


latin american robotics symposium | 2009

Dynamic gesture recognition for human robot interaction

Jong Lee-Ferng; Javier Ruiz-del-Solar; Rodrigo Verschae; Mauricio Correa

In this article a robust and real-time dynamic hand gesture recognition system meant to allow a natural interaction with a service robot, in dynamic environments, is proposed. The main novelty of the proposed approach is the use of temporal statistics about the hands positions and velocities as basic information to recognize the gestures. The use of these features allows carrying out the final recognition using a standard Bayes classifier, instead of the traditional Hidden Markov Models. A method for simultaneous gesture segmentation and recognition, which works by finding candidate subsequences that give high scores when matched to a gesture, is proposed. The system uses boosted classifiers to detect hands, and the mean-shift algorithm for their tracking. The systems performance is validated in a digit recognition system database and in real-world video sequences.


robot soccer world cup | 2015

An Episodic Long-Term Memory for Robots: The Bender Case

María-Loreto Sánchez; Mauricio Correa; Luz María Martínez; Javier Ruiz-del-Solar

The main goal of this paper is to propose a framework for providing an episodic long-term memory for a robot, which includes methods for acquiring, storing, updating, managing and using episodic information. This will give a robot the ability to incorporate past experiences when interacting with humans, so that the data that the robot learns transcends each session, and thus gives continuity to its activities and behaviors. As a proof of concept, the implementation of an episodic long-term memory for the Bender robot is described. This includes the implementation and evaluation of a behavior called Conversation, which allows Bender to interact with people using the information stored in the episodic memory.

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