While biases such as gender, race, and age have been closely examined in biometric recognition, especially in face and fingerprint traits, their exploration in gait-based recognition is lacking, except for one study. We formulate conditional-matched bootstrap analysis to control for confounding covariates like clothing style, height, and walking speed. The goal is to isolate genuine gender effects on gait recognition. We delve into gender-based disparities in gait recognition by using several state-of-the-art gait recognition methodologies - GaitSet, GaitPart, and GaitGL. For our analysis, the widely-referenced OU-MVLP dataset served as our foundation, which we enhanced with annotations about clothing style, body height, and walking speed. The results were illuminating. We observed a disparity in recognition performance across genders on the original dataset, with recognition for females higher than for males. However, after controlling for covariate distributions using conditional-matched bootstrap analysis, the gap was reduced, with clothing type emerging as the most significant contributor. Code available at https://github.com/azimIbragimov/gait-gender

In order to recognize people across long distances and from elevated viewpoints, biometric systems must handle the challenges of imaging through atmospheric turbulence and non-frontal presentations, in addition to the traditional A-PIE challenges of aging, pose, illumination, and expression. While individual biometric modalities such as facial appearance, gait, and whole body appearance each have a role to play, no single modality can address all of these challenges. This paper describes a novel multi-modal biometric recognition system that addresses the challenges of atmospheric turbulence, occlusions, and elevated viewpoints by combining these modalities. We demonstrate our system on both $R G B$ video-based identity verification and both open and closed-world search.

The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The recovery process is also expected to be rough. Economic activities impact social behaviors, which leave signatures in satellite images that can be automatically detected and classified. Satellite imagery can support the decision-making of analysts and policymakers by providing a different kind of visibility into the unfolding economic changes. In this article, we use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (CNNs) to recognize specific elements in satellite images that could be used to compute economic indicators based on it, automatically. This CNN ensemble framework ranked third place in the US Department of Defense xView challenge, the most advanced benchmark for object detection in satellite images. We show the potential of our framework for temporal analysis using the US IARPA Function Map of the World (fMoW) dataset. We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators. Our code and annotated high-resolution aerial scenes before and after the outbreak are available on GitHub.1.https://github.com/maups/covid19-satellite-analysis.

Today's legal restrictions that protect the privacy of biometric data are hampering fingerprint recognition researches. For instance, all high-resolution fingerprint databases ceased to be publicly available. To address this problem, we present a novel hybrid approach to synthesize realistic, high-resolution fingerprints. First, we improved Anguli, a handcrafted fingerprint generator, to obtain dynamic ridge maps with sweat pores and scratches. Then, we trained a CycleGAN to transform these maps into realistic fingerprints. Unlike other CNN-based works, we can generate several images for the same identity. We used our approach to create a synthetic database with 7400 images in an attempt to propel further studies in this field without raising legal issues. We included sweat pore annotations in 740 images to encourage research developments in pore detection. In our experiments, we employed two fingerprint matching approaches to confirm that real and synthetic databases have similar performance. We conducted a human perception analysis where sixty volunteers could hardly differ between real and synthesized fingerprints. Given that we also favorably compare our results with the most advanced works in the literature, our experimentation suggests that our approach is the new state-of-the-art.

In this paper, we describe Hydra, an ensemble of convolutional neural networks (CNNs) for geospatial land classification. The idea behind Hydra is to create an initial CNN that is coarsely optimized but provides a good starting pointing for further optimization, which will serve as the Hydra's body. Then, the obtained weights are fine-tuned multiple times with different augmentation techniques, crop styles, and classes weights to form an ensemble of CNNs that represent the Hydra's heads. By doing so, we prompt convergence to different endpoints, which is a desirable aspect for ensembles. With this framework, we were able to reduce the training time while maintaining the classification performance of the ensemble. We created ensembles for our experiments using two state-of-the-art CNN architectures, residual network (ResNet), and dense convolutional networks (DenseNet). We have demonstrated the application of our Hydra framework in two data sets, functional map of world (FMOW) and NWPU-RESISC45, achieving results comparable to the state-of-the-art for the former and the best-reported performance so far for the latter. Code and CNN models are available at https://github.com/maups/hydra-fmow.

We propose a continuous biometric authentication framework that uses the Possibilistic C-Means (PCM) algorithm to guarantee that only authorized users can access a protected system. PCM is employed to cluster a history of biometric samples in two classes: genuine and impostor. The degree of membership of the current biometric sample to those classes is then used as a score, which is fused over time to reach a decision regarding the safety of the system. The main advantage of our approach is that it is training-free, and thus is applicable to any biometric feature that can be captured continuously without modification. We evaluated our system using 2D, 3D and NIR videos of faces and achieved results comparable to a training-based state-of-art work.

Convolution neural networks (CNN) are the main development in face recognition in recent years. However, their description capacities have been somewhat understudied. In this paper, we show that training CNN only with color images is enough to properly describe depth and near infrared face images by assessing the performance of three publicly available CNN models on these other modalities. Furthermore, we find that, despite displaying results comparable to the human performance on LFW, not all CNN behave like humans recognizing faces in other scenarios.

We present a novel method for newborn authentication that matches key points in different interdigital regions from palm prints or footprints. Then, the method hierarchically combines the scores for authentication. We also present a novel pore detector for key point extraction, named Dynamic Pore Filtering (DPF), that does not rely on expensive processing techniques and adapts itself to different sizes and shapes of pores. We evaluated our pore detector using four different datasets. The obtained results of the DPF when using newborn dermatoglyphic patterns (2400ppi) are comparable to the state-of-the-art results for adult fingerprint images with 1200ppi. For authentication, we used four datasets acquired by two different sensors, achieving true acceptance rates of 91.53% and 93.72% for palm prints and footprints, respectively, with a false acceptance rate of 0%. We also compared our results to our previous approach on newborn identification, and we considerably outperformed its results, increasing the true acceptance rate from 71% to 98%.

In this work, we automate a 3D reconstruction pipeline using two SURF-based approaches. The first approach uses SURF correspondences to pre-align multiple 3D scans from a same object without requiring manual labor. The second approach uses SURF correspondences to calibrate high resolution color images to 3D scans in order to improve the texture quality of the final 3D model. Both approaches succeeded in more than 95% of the test cases and were able to automatically identify incorrect results. Our pipeline has been widely used in several projects of cultural heritage and the proposed improvements are very important to allow its use in large collections. The proposed approaches were favorably compared to other methods and have been successfully applied in cultural heritage preservation of sculptures located in a UNESCO World Heritage site.

In this work we evaluate the influence of pose variation on 3D face reconstruction from a single image. To this end, we present a 3D reconstruction method that combines a fitting technique and a sparse 3D deformable model to estimate the 3D information of 2D images with large pose variations. For our experiments, we synthetically created 2D images by rendering 3D models from the BU-3DFE database in different points of view. Thus, we have a precise ground truth that allows performing a quantitative analysis of the reconstruction accuracy. Our experimental results show that the reconstruction achieves the highest accuracy when using half-frontal face images, and is also more robust to noise and incorrect facial landmarks positioning.

We present a scale-invariant face detection approach based on boosted cascade classifiers using range images as input. The detector was developed to be employed as a preliminary stage for any real-time 3D face recognition system. The required computation time for this task was considerably reduced by eliminating the need for scanning an input image in multiple scales. Our experiments were performed using two well-known databases, and the proposed approach was favorably compared against a state-of-the-art face detection approach. We achieved a detection rate of 99.9% with only 0.2% of the images presenting false detections. We also evaluated the detector performance in face images presenting large pose variations and obtained detection rates as high as when using frontal face images.

We present a 3D face reconstruction system that takes as input either one single view or several different views. Given a facial image, we first classify the facial pose into one of five predefined poses, then detect two anchor points that are then used to detect a set of predefined facial landmarks. Based on these initial steps, for a single view we apply a warping process using a generic 3D face model to build a 3D face. For multiple views, we apply sparse bundle adjustment to reconstruct 3D landmarks which are used to deform the generic 3D face model. Experimental results on the Color FERET and CMU multi-PIE databases confirm our framework is effective in creating realistic 3D face models that can be used in many computer vision applications, such as 3D face recognition at a distance.

This paper presents a novel automatic framework to perform 3D face recognition. The proposed method uses a Simulated Annealing-based approach (SA) for range image registration with the Surface Interpenetration Measure (SIM), as similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions: circular and elliptical areas around the nose, forehead, and the entire face region. Then, a modified SA approach is proposed taking advantage of invariant face regions to better handle facial expressions. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images composed of 4,007 images with different facial expressions. The experiments simulated both verification and identification systems and the results compared to those reported by state-of-the-art works. By using all of the images in the database, a verification rate of 96.5 percent was achieved at a False Acceptance Rate (FAR) of 0.1 percent. In the identification scenario, a rank-one accuracy of 98.4 percent was achieved. To the best of our knowledge, this is the highest rank-one score ever achieved for the FRGC v2 database when compared to results published in the literature.

We present a methodology for face segmentation and facial landmark detection in range images. Our goal was to develop an automatic process to be embedded in a face recognition system using only depth information as input. To this end, our segmentation approach combines edge detection, region clustering, and shape analysis to extract the face region, and our landmark detection approach combines surface curvature information and depth relief curves to find the nose and eye landmarks. The experiments were performed using the two available versions of the Face Recognition Grand Challenge database and the BU-3DFE database, in order to validate our proposed methodology and its advantages for 3-D face recognition purposes. We present an analysis regarding the accuracy of our segmentation and landmark detection approaches. Our results were better compared to state-of-the-art works published in the literature. We also performed an evaluation regarding the influence of the segmentation process in our 3-D face recognition system and analyzed the improvements obtained when applying landmark-based techniques to deal with facial expressions.

This paper focuses a comparative evaluation of our framework for 3D face recognition and state-of-theart systems. Our method uses a Simulated Annealing-based approach (SA) for range image registration with the surface interpenetration measure (SIM) as the similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions. Experiments were performed on the FRGC v2 database simulating both verification and identification systems and the obtained results were compared to those reported in the literature. By using all the images in the database, a verification rate of 95.9% was achieved, at a False Acceptance Rate (FAR) of 0.1%. In the identification scenario, a rank-one accuracy of 99.5% was obtained. To our knowledge, this is the best rank-one score obtained on the FRGC v2 database, as compared to previously published results.

The surface interpenetration measure (SIM) was recently proposed as a promising measure for 3D face matching, although using two limited, small range image databases. In this paper we present novel, more extensive experiments using the SIM in a well-known 3D face database available on the biometric experimentation environment (BEE) to confirm qualitatively that the SIM is a effective, discriminatory measure. The experiments were performed based on range image registration by using two different methods: iterative closest point (ICP) and simulated annealing (SA). By computing the SIM after the registration of two 3D face images one can identify if those images come from the same subject or not. With our SA-based approach we obtained high verification rate scores, which is indeed one of the main goals of the Face Recognition Grand Challenge 2006.