A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

Abstract

In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

“…The digital upconverter converts modulated data into an mm-wave signal (by the carrier signal of frequency f0), and the power amplifier amplifies and transmits the analog data through the antenna. A low-noise amplifier (LNA) and digital down converter amplify the receiving signal without noise and convert the mm-wave signal to baseband data, respectively [34]. The serial-to-parallel converter buffers data for DTCWT processing.…”