A Review on Enhancement of Face Recognition Technology in Biometrics

Viejo

et al. 2019

Study of emotions has gained interest in the field of sensory and consumer research. Accurate information can be obtained by studying physiological behavior along with self-reported-responses. The aim was to identify physiological and self-reported-responses towards visual stimuli and predict self-reported-responses using biometrics. Panelists (N = 63) were exposed to 12 images (ten from Geneva Affective PicturE Database (GAPED), two based on common fears) and a questionnaire (Face scale and EsSense). Emotions from facial expressions (FaceReaderTM), heart rate (HR), systolic pressure (SP), diastolic pressure (DP), and skin temperature (ST) were analyzed. Multiple regression analysis was used to predict self-reported-responses based on biometrics. Results showed that physiological along with self-reported responses were able to separate images based on cluster analysis as positive, neutral, or negative according to GAPED classification. Emotional terms with high or low valence were predicted by a general linear regression model using biometrics, while calm, which is in the center of emotion dimensional model, was not predicted. After separating images, positive and neutral categories could predict all emotional terms, while negative predicted Happy, Sad, and Scared. Heart Rate predicted emotions in positive (R2 = 0.52 for Scared) and neutral (R2 = 0.55 for Sad) categories while ST in positive images (R2 = 0.55 for Sad, R2 = 0.45 for Calm).

Section: Introductionmentioning

confidence: 99%

Effects of Imagery as Visual Stimuli on the Physiological and Emotional Responses

Viejo

et al. 2019

“…Three methods of face recognition can be implemented using OpenCV:Eigenfaces, Fisherfaces and Local Binary Patterns LBP (Local Binary Pattern) [12].…”

Section: Trainingmentioning

confidence: 99%

Smart Office Surveillance Robot using Face Recognition

Karishma¹,

Tjprc²

2018

IJMPERD

Computer Vision and Image Understanding

“…In recent years, facial recognition has become an active field of research that covers various disciplines, such as Biometry (Alim et al, 2017;Mehra and Charaya, 2016;Surekha et al, 2017), information security , law enforcement and surveillance , smartcards or controlled access (Frikha et al, 2016;Lin et al, 2016). Some facial recognition systems have focused on achieving very specific objectives, such as classifying the gender and the age of a person or recognizing facial expression from a human image.…”

Section: Introductionmentioning

confidence: 99%

A multi-agent system for the classification of gender and age from images

González‐Briones

Villarrubia

Paz

et al. 2018

The automatic classification of human images on the basis of age range and gender can be used in audiovisual content adaptation for Smart TVs or marquee advertising. Knowledge about users is used by publishing agencies and departments regulating TV content; on the basis of this information (age, gender) they are able to provide content that suits the interests of users. To this end, the creation of a highly precise image pattern recognition system is necessary, this may be one of the greatest challenges faced by computer technology in the last decades. These recognition systems must apply different pattern recognition techniques, in order to distinct gender and age in the images. In this work, we propose a multi-agent system that integrates different techniques for the acquisition, preprocessing and processing of images for the classification of age and gender. The system has been tested in an office building. Thanks to the use of a multi-agent system which allows to apply different workflows simultaneously, the performance of different methods could be compared (each flow with a different configuration). Experimental results have confirmed that a good preprocessing stage is necessary if we want the classification methods to perform well (Fisherfaces, Eigenfaces, Local Binary Patterns, Multilayer perceptron). The Fisherfaces method has proved to be more effective than MLP and the training time was shorter. In terms of the classification of age, Fisherfaces offers the best results in comparison to the rest of the system's classifiers. The use of filters has allowed to reduce dimensionality, as a result the workload was reduced, a great advantage in a system that performs classification in real time.