33 research outputs found
Facial emotion recognition using min-max similarity classifier
Recognition of human emotions from the imaging templates is useful in a wide
variety of human-computer interaction and intelligent systems applications.
However, the automatic recognition of facial expressions using image template
matching techniques suffer from the natural variability with facial features
and recording conditions. In spite of the progress achieved in facial emotion
recognition in recent years, the effective and computationally simple feature
selection and classification technique for emotion recognition is still an open
problem. In this paper, we propose an efficient and straightforward facial
emotion recognition algorithm to reduce the problem of inter-class pixel
mismatch during classification. The proposed method includes the application of
pixel normalization to remove intensity offsets followed-up with a Min-Max
metric in a nearest neighbor classifier that is capable of suppressing feature
outliers. The results indicate an improvement of recognition performance from
92.85% to 98.57% for the proposed Min-Max classification method when tested on
JAFFE database. The proposed emotion recognition technique outperforms the
existing template matching methods
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
The memristive crossbar aims to implement analog weighted neural network,
however, the realistic implementation of such crossbar arrays is not possible
due to limited switching states of memristive devices. In this work, we propose
the design of an analog deep neural network with binary weight update through
backpropagation algorithm using binary state memristive devices. We show that
such networks can be successfully used for image processing task and has the
advantage of lower power consumption and small on-chip area in comparison with
digital counterparts. The proposed network was benchmarked for MNIST
handwritten digits recognition achieving an accuracy of approximately 90%
Real-time Analog Pixel-to-pixel Dynamic Frame Differencing with Memristive Sensing Circuits
In this paper, we propose an analog pixel differencing circuit for
differentiating pixels between frames directly from CMOS pixels. The analog
information processing at sensor is a topic of growing appeal to develop edge
AI devices. The proposed circuit is integrated into a pixel-parallel and
pixel-column architectures. The proposed system is design using TSMC
CMOS technology. The power dissipation of the proposed circuit is ,
and on-chip ares is . The architectures are tested for moving
object detection application.Comment: IEEE SENSORS 201