Modesto Castrillón Santana

University of Las Palmas de Gran Canaria | ULPGC · Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

In the Human-Machine Interactions (HMI) landscape, understanding user emotions is pivotal for elevating user experiences. This paper explores Facial Expression Recognition (FER) within HMI, employing a distinctive multimodal approach that integrates visual and auditory information. Recognizing the dynamic nature of HMI, where situations evolve, thi...

Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address these challenges. Our approach initially trains a model on the large-scale synthetic dataset, RoadSens...

Pedestrian Attribute Recognition (PAR) poses a significant challenge in developing automatic systems that enhance visual surveillance and human interaction. In this study, we investigate using Visual Question Answering (VQA) models to address the zero-shot PAR problem. Inspired by the impressive results achieved by a zero-shot VQA strategy during t...

Person re-identification has gained significant attention in recent years due to its numerous practical applications in video surveillance. However, while artificial intelligence and deep learning methods have enabled substantial progress in particular aspects of this domain, putting together those individual advances to generate practical systems...

Emotion classification through EEG signals plays a significant role in psychology, neuroscience, and human-computer interaction. This paper addresses the challenge of mapping human emotions using EEG data in the Mapping Human Emotions through EEG Signals FG24 competition. Subjects mimic the facial expressions of an avatar, displaying fear, joy, ang...

Person re-identification (ReID) is a popular area of research in the field of computer vision. Despite the significant advancements achieved in recent years, most of the current methods rely on datasets containing subjects captured with good lighting under static conditions. ReID presents a significant challenge in real-world sporting scenarios, su...

In conventional machine learning (ML), a fundamental assumption is that the training and test sets share identical feature distributions, a reasonable premise drawn from the same dataset. However, real-world scenarios often defy this assumption, as data may originate from diverse sources, causing disparities between training and test data distribut...

As the interest in robots continues to grow across various domains, including healthcare, construction and education, it becomes crucial to prioritize improving user experience and fostering seamless interaction. These human-machine interactions (HMI) are often impersonal. Our proposal, built upon previous work in the field, aims to use biometric d...

Background Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mi...

Pedestrian attribute recognition (PAR) ensures public safety and security. By automatically detecting attributes such as clothing color, accessories, and hairstyles, surveillance systems can provide valuable information for criminal investigations, aiding in identifying suspects based on their appearances. Additionally, in crowd management scenario...

Action recognition models and cumulative race time (CRT) are practical tools in sports analytics, providing insights into athlete performance, training, and strategy. Measuring CRT allows for identifying areas for improvement, such as specific sections of a racecourse or the effectiveness of different strategies. Human action recognition (HAR) algo...

Facial expressions are dynamic processes that evolve over temporal segments, including onset, apex, offset, and neutral. However, previous works on automatic facial expression analysis have mainly focused on the recognition of discrete emotions, neglecting the continuous nature of these processes. Additionally, facial images captured from videos in...

We present a transfer learning analysis on a sporting environment of the expanded 3D (X3D) neural networks. Inspired by action quality assessment methods in the literature, our method uses an action recognition network to estimate athletes' cumulative race time (CRT) during an ultra-distance competition. We evaluate the performance considering the...

Smartphones contain personal and private data to be protected, such as everyday communications or bank accounts. Several biometric techniques have been developed to unlock smartphones, among which ear biometrics represents a natural and promising opportunity even though the ear can be used in other biometric and multi-biometric applications. A prob...

The uncontrolled characteristics of long-term scenarios, like ultra-running competitions, are challenging for person re-identification approaches based on computer vision methods. State-of-the-art techniques have reported hardly moderate success for whole-body runner re-identification due to the existence of distinct illumination conditions, as wel...

Background: Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to m...

The scientific community and mass media have already reported the use of nonverbal behavior analysis in sports for athletes’ performance. Their conclusions stated that certain emotional expressions are linked to athlete’s performance, or even that psychological strategies serve to improve endurance performance. This paper examines the portrayal of...

Predicting an athlete's performance based on short footage is highly challenging. Performance prediction requires high domain knowledge and enough evidence to infer an appropriate quality assessment. Sports pundits can often infer this kind of information in real-time. In this paper, we propose regressing an ultra-distance runner cumulative race ti...

In May 2021, the site runnersworld.com published that participation in ultra-distance races has increased by 1,676% in the last 23 years. Moreover, nearly 41% of those runners participate in more than one race per year. The development of wearable devices has undoubtedly contributed to motivating participants by providing performance measures in re...

In May 2021, the site runnersworld.com published that participation in ultra-distance races has increased by 1,676% in the last 23 years. Moreover, nearly 41% of those runners participate in more than one race per year. The development of wearable devices has undoubtedly contributed to motivating participants by providing performance measures in re...

According to the Wall Street Journal, one billion surveillance cameras will be deployed around the world by 2021. This amount of information can be hardly managed by humans. Using a Inflated 3D ConvNet as backbone, this paper introduces a novel automatic violence detection approach that outperforms state-of-the-art existing proposals. Most of those...

In this paper, we tackle the task of improving biometric verification in the context of Human-Robot Interaction (HRI). A robot that wants to identify a specific person to provide a service can do so by either image verification or, if light conditions are not favourable, through voice verification. In our approach, we will take advantage of the pos...

The quantification of microplastics is a needed task to monitor its evolution and model its behavior. However, it is a time demanding task traditionally performed using expensive equipment. In this paper, an architecture based on deep learning networks is presented with the aim of automatically count and classify microplastic particles in the range...

Person re-identification (Re-ID) is the task of retrieving a person of interest taken from different cameras or from the same camera in different occasions. To address this challenging task, a large amount of labelled data is required both for testing and for learning. Such high quality annotated data is still rare for many Re-ID applications. In t...

User cooperative behaviour is mandatory and valuable to warranty data acquisition quality in forensic biometrics. In the present paper, we consider human cooperative behaviour in front of wearable security cameras. Moreover, we propose a human cooperation detection pipeline based on deep learning. Recently, recurrent neural networks (RNN) have show...

GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robo...

The growing need of security in large open spaces led to the need to use video capture of people in different context and illumination and with multiple biometric traits as head pose, body gait, eyes, nose, mouth, and further more. All these traits are useful for a multibiometric identification or a person re-identification in a video surveillance...

The management of plastic debris is a serious issue due to its durability. Unfortunately, million tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to monitor the amount of plastic in beaches is to collect samples and visually count and sort the plastic particles present in them. This is a very ti...

From border controls to personal devices, from online exam proctoring to human-robot interaction, biometric technologies are empowering individuals and organizations with convenient and secure authentication and identification services. However, most biometric systems leverage only a single modality, and may face challenges related to acquisition d...

Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with...

Transparency laws facilitate citizens to monitor the activities of political representatives. In this sense, automatic or manual diarization of parliamentary sessions is required, the latter being time consuming. In the present work, this problem is addressed as a person re-identification problem. Re-identification is defined as the process of matc...

With the rapid growth of online videos on the Web, there is an increasing research interest in automatic categorisation of videos. It is essential for multimedia tasks in order to facilitate indexing, search and retrieval of available video files on the Web. In this paper, we propose a different technique for the video categorisation problem using...

Microplastic particles have become an important ecological problem due to the huge amount of plastics debris that ends up in the sea. An additional impact is the ingestion of microplastics by marine species, and thus microplastics enter into the food chain with unpredictable effects on humans. In addition to the exploration of their presence in fis...

The speaker activity at the Canary Islands Parliament is recorded, and later manually annotated. This task can be modelled as a diarization problem, that is a way to automatically annotated who and when is speaking. In this paper, we propose the use of the visual cue to solve the diarization task. To perform this approach, it is mandatory to detect...

In the present paper, we propose a source camera identification method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed...

Parliamentary websites have become one of the most important windows for citizens and media to follow the activities of their legislatures and to hold parliaments to account. Therefore, most parliamentary institutions aim to provide new multimedia solutions capable of displaying video fragments on demand on plenary activities. This paper presents a...

The recent evolution of storage devices, digital embedded cameras and the Internet have collaterally allowed sexual predators to take advantage of these technological breakthroughs to gather illegal media, which is exhibited uncensored through Peer-to-Peer file sharing networks. In this paper, we are particularly concerned about the increasing avai...

The 2015 FRVT gender classification (GC) report evidences the problems that current approaches tackle in situations with large variations in pose, illumination, background and facial expression. The report suggests that both commercial and research solutions are hardly able to reach an accuracy over 90 % for The Images of Groups dataset, a proven s...

The 2016 International Conference on Pattern Recognition hosted the MICHE-II Contest, with the aim at biometric identification on mobile devices. This paper describes the ideas behind one of the contest submissions, in particular the one ranked 6th (4th in cross-device), including different novelties in relation to the original proposal. Our approa...

Color based re-identification methods usually rely on a distance function to measure the similarity between individuals. In this paper we study the behavior of several histogram distance measures in different color spaces. We wonder whether there is a particular histogram distance measure better than others, likewise also, if there is a color space...

Several applications require demographic information of ordinary people in unconstrained scenarios. This is not a trivial task due to significant human appearance variations. In this work, we introduce trixels for clustering image regions, enumerating their advantages compared to superpixels. The classical GrabCut algorithm is later modified to seg...

Gender classification (GC) has achieved high accuracy in different experimental evaluations based mostly on inner facial details. However, these results do not generalize well in unrestricted datasets and particularly in cross-database experiments, where the performance drops drastically. In this paper, we analyze the state-of-the-art GC accuracy o...

Automatic labelling of speakers is an essential task for speakers diarization in parliamentary debates given the huge amount of video data to annotate. In this paper, we address the speaker diarization problem as a visual speaker re-identification issue with a special emphasis on the analysis of different shot types. We propose two approaches that...

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 app...

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 r...

In this paper we focus on gender classification from face images, which is still a challenging task in unrestricted scenarios. This task can be useful in a number of ways, e.g., as a preliminary step in biometric identity recognition supported by demographic information. We compare a feature based approach with two score based ones. In the former,...

Gender classification (GC) in the wild is an active area of current research. In this paper, we focus on the combination of a holistic state of the art approach based on features extracted from the facial pattern, with patch based approaches that focus on inner facial areas. Those regions are selected for being relevant to the human system accordin...

Resumen de resultados obtenidos en los experimentos de estimación de grupo de edad, llevados a cabo por los estudiantes matriculados en la asignatura Sistemas de Percepción del Máster Oficial de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería en el curso 2014/2015.

Gender classification (GC) has achieved high accuracy in different experimental evaluations based mostly on inner facial details. However, these results are not generalized in unrestricted datasets and particularly in cross-database experiments, where the performance drops drastically. In this paper, we analyze the state-of-the-art GC accuracy on t...

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...

Facial expression recognition is one of the most challenging research areas in the image recognition field and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human-machine interac...

The automatic extraction of biometric descriptors of anonymous people is a challenging scenario in camera networks. This task is typically accomplished making use of visual information. Calibrated RGBD sensors make possible the extraction of point cloud information. We present a novel approach for people semantic description and re-identification u...