Learning Representations for Face Recognition: A Review from Holistic to Deep Learning

For decades, researchers have investigated how to recognize facial images. This study reviews the development of different face recognition (FR) methods, namely, holistic learning, handcrafted local feature learning, shallow learning, and deep learning (DL). With the development of methods, the accuracy of recognizing faces in the labeled faces in the wild (LFW) database has been increased. The accuracy of holistic learning is 60%, that of handcrafted local feature learning increases to 70%, and that of shallow learning is 86%. Finally, DL achieves human-level performance (97% accuracy). This enhanced accuracy is caused by large datasets and graphics processing units (GPUs) with massively parallel processing capabilities. Furthermore, FR challenges and current research studies are discussed to understand future research directions. The results of this study show that presently the database of labeled faces in the wild has reached 99.85% accuracy

Impedance Simulator for Testing of Instruments for Bioimpedance Sensing

Abstract: Bioimpedance sensing is a noninvasive technique for measuring parameters related to tissue structure or physiological events. Generally, the impedance is sensed by injecting a high frequency low intensity current through a pair of electrodes placed across the selected region of the body and monitoring the voltage developed across the same or another pair of electrodes. The base value of the impedance and its variation can be used, with the help of an appropriate model, for obtaining diagnostic information. For testing and calibration of instruments developed for bioimpedance sensing, we have developed an impedance simulator by using a microcontroller and analog switches. It can be used for measuring sensitivity and frequency response for bioimpedance signals, and for studying the effect of various electrode configurations and common mode interference caused by bioelectric sources and external pickups

Modified Digital Correlation Technique for Accurate Phase Measurement in Multi-Frequency Bio-Impedance Analysis

In bio-impedance analysis (BIA), high-frequency low-amplitude alternating current (AC) signals can incur time delays due to the capacitive nature of human cell membranes, and the characteristics of human tissues can be assessed from these delays in terms of phase changes. To accurately measure the phase changes, this work proposes a modified digital correlation-based phase measurement method. The accuracy of the general correlation technique is improved through digital direct synthesis (DDS) and digital correlation of unipolar square input signals. The proposed method is established through memory management and frequency adjustment. The result shows that, compared to the existing methods, the proposed method needs fewer hardware components, has better accuracy of 0.2° and higher frequency compatibility from 5 kHz to 1 MHz, and requires lower cost (140 USD). The method can be applied for the BIA of all types of tissues (recently used in COVID detection and care) and for the applications where efficient phase measurement is required