Modesto Castrillón-Santana

Competition on counter measures to 2-D facial spoofing attacks

Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method. Six different teams from universities around the world have participated in the contest. Use of one or multiple techniques from motion, texture analysis and liveness detection appears to be the common trend in this competition. Most of the algorithms are able to clearly separate spoof attempts from real accesses. The results suggest the investigation of more complex attacks.

The FG 2015 Kinship Verification in the Wild Evaluation

The aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015 IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised, image-restricted, and image-unrestricted) were designed. Five institutions submitted their results to the evaluation: (i) Politecnico di Torino, Italy; (ii) LIRIS-University of Lyon, France; (iii) Universidad de Las Palmas de Gran Canaria, Spain; (iv) Nanjing University of Aeronautics and Astronautics, China; and (v) Bar Ilan University, Israel. Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers.

Multiple face detection at different resolutions for perceptual user interfaces

This paper describes in detail a real-time multiple face detection system for video streams. The system adds to the good performance provided by a window shift approach, the combination of different cues available in video streams due to temporal coherence. The results achieved by this combined solution outperform the basic face detector obtaining a 98% success rate for around 27000 images, providing additionally eye detection and a relation between the successive detections in time by means of detection threads.

Kinship verification in the wild: The first kinship verification competition

Kinship verification from facial images in wild conditions is a relatively new and challenging problem in face analysis. Several datasets and algorithms have been proposed in recent years. However, most existing datasets are of small sizes and one standard evaluation protocol is still lack so that it is difficult to compare the performance of different kinship verification methods. In this paper, we present the Kinship Verification in the Wild Competition: the first kinship verification competition which is held in conjunction with the International Joint Conference on Biometrics 2014, Clearwater, Florida, USA. The key goal of this competition is to compare the performance of different methods on a new-collected dataset with the same evaluation protocol and develop the first standardized benchmark for kinship verification in the wild.

Improving Gender Classification Accuracy in the Wild

In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Different descriptors, resolutions and classifiers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

Gender Classification in Large Databases

In this paper, we address the challenge of gender classification using large databases of images with two goals. The first objective is to evaluate whether the error rate decreases compared to smaller databases. The second goal is to determine if the classifier that provides the best classification rate for one database, improves the classification results for other databases, that is, the cross-database performance.

Automatic initialization for facial analysis in interactive robotics

The human face plays an important role in communication as it allows to discern different interaction partners and provides nonverbal feedback. In this paper, we present a soft real-time vision system that enables an interactive robot to analyze faces of interaction partners not only to identify them, but also to recognize their respective facial expressions as a dialog-controlling non-verbal cue. In order to assure applicability in real world environments, a robust detection scheme is presented which detects faces and basic facial features such as the position of the mouth, nose, and eyes. Based on these detected features, facial parameters are extracted using active appearance models (AAMs) and conveyed to support vector machine (SVM) classifiers to identify both persons and facial expressions. This paper focuses on four different initialization methods for determining the initial shape for the AAM algorithm and their particular performance in two different classification tasks with respect to either the facial expression DaFEx database and to the real world data obtained from a robots point of view.

On using periocular biometric for gender classification in the wild

The periocular area is a reliable cue for automatic gender classification (GC).Each local descriptor and grid configuration report different GC accuracy.The score level fusion of local descriptors increases GC performance.Tests carried out in a challenging large and unrestricted dataset.The fusion of periocular and facial GC reduces the classification error in roughly 20%. Display Omitted Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild. The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem. As expected, the achieved results are slightly worse, roughly 8 percentage points lower, than those obtained by state-of-the-art facial GC, but they suggest the validity of the periocular area particularly in difficult scenarios where the whole face is not visible, or has been altered. A final experiment combines in a multi-scale approach features extracted from the periocular, face and head and shoulders areas, fusing them in a two stage ensemble of classifiers. The accuracy reported beats any previous results on the difficult The Images of Groups dataset, reaching 92.46%, with a GC error reduction of almost 20% compared to the best face based GC results in the literature.

An study on ear detection and its applications to face detection

OpenCV includes different object detectors based on the Viola-Jones framework. Most of them are specialized to deal with the frontal face pattern and its inner elements: eyes, nose, and mouth. In this paper, we focus on the ear pattern detection, particularly when a head profile or almost profile view is present in the image. We aim at creating real-time ear detectors based on the general object detection framework provided with OpenCV. After training classifiers to detect left ears, right ears, and ears in general, the performance achieved is valid to be used to feed not only a head pose estimation system but also other applications such as those based on ear biometrics.

MEG: Texture operators for multi-expert gender classification

Abstract In this paper we focus on gender classification from face images. Despite advances in equipment as well as methods, automatic face image processing for recognition or even just for the extraction of demographics, is still a challenging task in unrestricted scenarios. Our tests are aimed at carrying out an extensive comparison of a feature based approach with two score based ones. When directly using features, we first apply different operators to extract the corresponding feature vectors, and then stack such vectors. These are classified by a SVM-based approach. When using scores, the different operators are applied in a completely separate way, so that each of them produces the corresponding scores. Answers are then either fed to a SVM, or compared pairwise to exploit Likelihood Ratio. The testbeds used for experiments are EGA database, which presents a good balance with respect to demographic features of stored face images, and GROPUS, an increasingly popular benchmark for massive experiments. The obtained performances confirm that feature level fusion achieves an often better classification accuracy. However, it is computationally expensive. We contribute to the research on this topic in three ways: 1) we show that the proposed score level fusion approaches, though less demanding, can achieve results that are comparable to feature level fusion, or even slightly better given that we fuse a particular set of experts; the main advantage over the feature-based approach relying on chained vectors, is that it is not required to evaluate a complex multi-feature distribution and the training process: thanks to the individual training of experts the overall process is more efficient and flexible, since experts can be easily added or discarded from the final architecture; 2) we evaluate the number of uncertain/ambiguous cases, i.e., those that might cause classification errors depending on the classification thresholds used, and show that with our score level fusion these significantly decreases; despite the final rate of correct classifications, this results in a more robust system; 3) we achieve very good results with operators that are not computationally expensive.