Expression Recognition Using the Periocular Region: A Feasibility Study

Abstract

This paper investigates the feasibility of using the periocular region for expression recognition. Most works have tried to solve this by analyzing the whole face. Periocular is the facial region in the immediate vicinity of the eye. It has the advantage of being available over a wide range of distances and under partial face occlusion, thus making it suitable for unconstrained or uncooperative scenarios. We evaluate five different image descriptors on a dataset of 1,574 images from 118 subjects. The experimental results show an average/overall accuracy of 67.0/78.0% by fusion of several descriptors. While this accuracy is still behind that attained with full-face methods, it is noteworthy to mention that our initial approach employs only one frame to predict the expression, in contraposition to state of the art, exploiting several order more data comprising spatial-temporal data which is often not available.

Biometrics in the Era of COVID-19: Challenges and Opportunities

“…Therefore, the authors indicated that the fusion of information stemming form different architectures may improve the performance of the periocular region, making it eventually similar to that of unoccluded facial images. Similarly, the periocular region can be also utilised to estimate emotions using handcrafted textural features [62] or deep learning [63].…”

Biometrics in the Era of COVID-19: Challenges and Opportunities

“…Therefore, the authors indicated that the fusion of information stemming form different architectures may improve the performance of the periocular region, making it eventually similar to that of unoccluded facial images. Similarly, the periocular region can be also utilised to estimate emotions using handcrafted textural features [51] or deep learning [52].…”

AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control

“…It can also aid for example to assess that an authorized person is driving a vehicle, or to detect driver drowsiness or distraction. Here, modalities captured with cameras (face Li and Deng, 2020) or eye regions (Alonso-Fernandez et al, 2018;Alonso-Fernandez and Bigun, 2016)) can be complemented with sensors attached to the seat or the steering wheel that allows to capture bio-signals such as heartbeats (Wartzek et al, 2011) or skin impedance (Macias et al, 2013), which correlates, for example, with sweating -stress level -but also with fitness levels (Jaffrin and Morel, 2008). There are also proof of concepts using Doppler radar for vital signs measurement , which has the evident advantage of not needing any type of contact.…”