Construcción de un conjunto de imágenes faciales con metadatos biométricos y rasgos étnicos de ecuatorianos

Actualmente, las técnicas de Machine Learning son cada vez más usadas en diversas áreas de la ciencia y la sociedad, su funcionamiento está basado en la recolección y análisis sistemático de datos que permiten crear modelos de predicción. Hoy en día existen diversas fuentes para adquirir datos/imágenes, como los repositorios públicos de gobiernos, instituciones académicas, centros de investigación y datos generados por empresas, entre otros. A pesar de todas estas opciones no siempre se tienen los datos adecuados para la construcción de los modelos, más aún cuando se abordan problemas que requieren el uso de imágenes. Este artículo propone la construcción de un conjunto de imágenes faciales pertenecientes a las etnias más representativas del Ecuador: afro-ecuatorianos, mestizos, indígenas y blancos (europeo-descendientes). Además, se incluyen metadatos referentes a los rasgos faciales característicos de cada etnia obtenidos a través de la aplicación de una encuesta y las biométricas respectivas calculadas por medio de la ejecución de modelos ssd inception v2 coco y faster rcnn inception v2 coco, de la librería TensorFlow. El conjunto de imágenes resultante cuenta con 430 instancias, cada una con 24 atributos. Los resultados en la detección y adquisición de las biométricas del rostro fueron contrastados con mediciones físicas, consiguiendo errores inferiores al 10%.Currently, Machine Learning techniques are increasingly used in various areas of science and society, its operation is based on the systematic collection and analysis of data that allow create prediction models. Today, there are several sources to acquire data / images, such as public repositories of governments, academic institutions, research centers and data generated by companies, among others. Despite all these options, the adequate data for the construction of the models are not always available, especially when problems that require the use of images are addressed. This article proposes the construction of a set of facial images of Ecuadorians belonging to the most representative ethnic groups of Ecuador: afro-ecuadorians, mestizos, indigenous and european-descendants. In addition, metadata referring to the facial features of each ethnic group obtained through the application of a survey and the respective biometrics, calculated through the execution of ssd inception v2 coco and faster rcnn inception v2 coco, models of the TensorFlow library are included. The resulting image dataset has 430 instances, each with 24 attributes. The results in the detection and acquisition of the face biometrics were contrasted with physical measurements, achieving errors less than 10%

A Reminiscence of ”Mastermind”: Iris/Periocular Biometrics by ”In-Set” CNN Iterative Analysis

Convolutional neural networks (CNNs) have emerged as the most popular classification models in biometrics research. Under the discriminative paradigm of pattern recognition, CNNs are used typically in one of two ways: 1) verification mode (”are samples from the same person?”), where pairs of images are provided to the network to distinguish between genuine and impostor instances; and 2) identification mode (”whom is this sample from?”), where appropriate feature representations that map images to identities are found. This paper postulates a novel mode for using CNNs in biometric identification, by learning models that answer to the question ”is the query’s identity among this set?”. The insight is a reminiscence of the classical Mastermind game: by iteratively analysing the network responses when multiple random samples of k gallery elements are compared to the query, we obtain weakly correlated matching scores that - altogether - provide solid cues to infer the most likely identity. In this setting, identification is regarded as a variable selection and regularization problem, with sparse linear regression techniques being used to infer the matching probability with respect to each gallery identity. As main strength, this strategy is highly robust to outlier matching scores, which are known to be a primary error source in biometric recognition. Our experiments were carried out in full versions of two well known irises near-infrared (CASIA-IrisV4-Thousand) and periocular visible wavelength (UBIRIS.v2) datasets, and confirm that recognition performance can be solidly boosted-up by the proposed algorithm, when compared to the traditional working modes of CNNs in biometrics.info:eu-repo/semantics/publishedVersio