Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications(e.g.,sur-veillance and human–computer interaction[HCI]).Images of varying levels of illumination,occlusion,and other factors are captured in uncontrolled environ-ments.Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances,and faces may be covered by a scarf,hijab,or mask due to the COVID-19 pandemic.The periocular region is a reliable source of information because it features rich discriminative biometric features.However,most existing gender classification approaches have been designed based on hand-engineered features or validated in controlled environ-ments.Motivated by the superior performance of deep learning,we proposed a new method,PeriGender,inspired by the design principles of the ResNet and DenseNet models,that can classify gender using features from the periocular region.The proposed system utilizes a dense concept in a residual model.Through skip connections,it reuses features on different scales to strengthen dis-criminative features.Evaluations of the proposed system on challenging datasets indicated that it outperformed state-of-the-art methods.It achieved 87.37%,94.90%,94.14%,99.14%,and 95.17%accuracy on the GROUPS,UFPR-Periocular,Ethnic-Ocular,IMP,and UBIPr datasets,respectively,in the open-world(OW)protocol.It further achieved 97.57%and 93.20%accuracy for adult periocular images from the GROUPS dataset in the closed-world(CW)and OW protocols,respectively.The results showed that the middle region between the eyes plays a crucial role in the recognition of masculine features,and feminine features can be identified through the eyebrow,upper eyelids,and corners of the eyes.Furthermore,using a whole region without cropping enhances PeriGender’s learning capability,improving its understanding of both eyes’global structure without discontinuity.

Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

The detection of alcoholism is of great importance due to its effects on individuals and society.Automatic alcoholism detection system(AADS)based on electroencephalogram(EEG)signals is effective,but the design of a robust AADS is a challenging problem.AADS’current designs are based on conventional,hand-engineered methods and restricted performance.Driven by the excellent deep learning(DL)success in many recognition tasks,we implement an AAD system based on EEG signals using DL.A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD problem.In order to solve this problem,we propose a multi-channel Pyramidal neural convolutional(MP-CNN)network that requires a less number of learnable parameters.Using the deep CNN model,we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or normal.We validate the robustness and effectiveness of proposed AADS using KDD,a benchmark dataset for alcoholism detection problem.In order to find the brain region that contributes significant role in AAD,we investigated the effects of selected 19 EEG channels(SC-19),those from the whole brain(ALL-61),and 05 brain regions,i.e.,TEMP,OCCIP,CENT,FRONT,and PERI.The results show that SC-19 contributes significant role in AAD with the accuracy of 100%.The comparison reveals that the state-of-the-art systems are outperformed by the AADS.The proposed AADS will be useful in medical diagnosis research and health care systems.