An efficient deep learning technique for facial emotion recognition

Emotion recognition from facial images is considered as a challenging task due to the varying nature of facial expressions. The prior studies on emotion classification from facial images using deep learning models have focused on emotion recognition from facial images but face the issue of performance degradation due to poor selection of layers in the convolutional neural network model.To address this issue, we propose an efficient deep learning technique using a convolutional neural network model for classifying emotions from facial images and detecting age and gender from the facial expressions efficiently. Experimental results show that the proposed model outperformed baseline works by achieving an accuracy of 95.65% for emotion recognition, 98.5% for age recognition, and 99.14% for gender recognition.

     

Scattering of Beltrami fields by an orthorhombic dielectric-magnetic sphere immersed in a chiral medium

The T matrix method can be formulated to study Beltrami planewave scattering by a sphere composed of an orthorhombic dielectric magnetic material immersed in a chiral medium. Whereas an orthorhombic dielectric-magnetic material whose permeability dyadic is a scalar multiple of its permittivity dyadic is pathologically unirefringent and anisotropic. A chiral medium characterized by either a left-handedness or a right-handedness in its microstructure is birefringent and not anisotropic. The backscattering efficiency has an undulating behaviour with increase in electrical size and is highly affected by constitutive anisotropy of the sphere. Multiple lobes appear in theplots of the differential scattering efficiency when the incident ?eld is left-circularly polarized wave. Peaks of curves of the backscattering effciency appear at lower frequencies for an incident left-circularly polarized wave and at higher frequencies for a right-circularly polarized wave incidence, if the sphere is impedance-matched to the ambient chiral medium.