scholarly journals Robust Face Recognition Against Soft-errors Using a Cross-layer Approach

2016 ◽  
Vol 11 (5) ◽  
pp. 657
Author(s):  
Gu-Min Jeong ◽  
Chang-Woo Park ◽  
Sang-Il Choi ◽  
Kyoungwoo Lee ◽  
Nikil Dutt
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Error Detection ◽  
Soft Errors ◽  
Memory Systems ◽  
Recognition System ◽  
Soft Error ◽  
Multimedia Systems ◽  
Cross Layer ◽  
Robust Face Recognition

Recently, soft-errors, temporary bit toggles in memory systems, have become increasingly important. Although soft-errors are not critical to the stability of recognition systems or multimedia systems, they can significantly degrade the system performance. Considering these facts, in this paper, we propose a novel method for robust face recognition against soft-errors using a cross layer approach. To attenuate the effect of soft-errors in the face recognition system, they are detected in the embedded system layer by using a parity bit checker and compensated in the application layer by using a mean face. We present the soft-error detection module for face recognition and the compensation module based on the mean face of the facial images. Simulation results show that the proposed system effectively compensates for the performance degradation due to soft errors and improves the performance by 2.11 % in case of the Yale database and by 10.43 % in case of the ORL database on average as compared to that with the soft-errors induced.

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

2008 ◽  
pp. 188-211
Author(s):  
Ting Shan ◽  
Abbas Bigdeli ◽  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Principal Component ◽  
Recognition System ◽  
Active Appearance Models ◽  
Face Recognition System ◽  
The Face ◽  
The Embedded System ◽  
Computationally Intensive ◽  
Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

scholarly journals Optimized switch-level soft error detection based on advanced switch-level models

2021 ◽  
Author(s):  
Jalal Mohammad Chikhe
Keyword(s):  
Error Detection ◽  
Early Stage ◽  
Soft Errors ◽  
Logic Gate ◽  
Soft Error ◽  
Combinational Circuits ◽  
Single Event Transient ◽  
New Techniques ◽  
Speed Up ◽  
Logic Threshold

Due to the reduction of transistor size, modern circuits are becoming more sensitive to soft errors. The development of new techniques and algorithms targeting soft error detection are important as they allow designers to evaluate the weaknesses of the circuits at an early stage of the design. The project presents an optimized implementation of soft error detection simulator targeting combinational circuits. The developed simulator uses advanced switch level models allowing the injection of soft errors caused by single event-transient pulses with magnitudes lesser than the logic threshold. The ISCAS'85 benchmark circuits are used for the simulations. The transients can be injected at drain, gate, or inputs of logic gate. This gives clear indication of the importance of transient injection location on the fault coverage. Furthermore, an algorithm is designed and implemented in this work to increase the performance of the simulator. This optimized version of the simulator achieved an average speed-up of 310 compared to the non-algorithm based version of the simulator.

Makeup Invariant Face Recognition using Features from Accelerated Segment Test and Eigen Vectors

2017 ◽  
Vol 17 (01) ◽  
pp. 1750005 ◽  
Author(s):  
Aruna Bhat
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Training Data ◽  
Data Sets ◽  
Security Breaches ◽  
Daunting Task ◽  
Eigen Values ◽  
The Face ◽  
Robust Face Recognition

A methodology for makeup invariant robust face recognition based on features from accelerated segment test and Eigen vectors is proposed. Makeup and cosmetic changes in face have been a major cause of security breaches since long time. It is not only difficult for human eyes to catch an imposter but also an equally daunting task for a face recognition system to correctly identify an individual owing to changes brought about in face due to makeup. As a crucial pre-processing step, the face is first divided into various segments centered on the eyes, nose, lips and cheeks. FAST algorithm is then applied over the face images. The features thus derived from the facial image act as the fiducial points for that face. Thereafter principal component analysis is applied over the set of fiducial points in each segment of every face image present in the data sets in order to compute the Eigen vectors and the Eigen values. The resultant principal component which is the Eigen vector with the highest Eigen value yields the direction of the features in that segment. The principal components thus obtained using fiducial points generated from FAST in each segment of the test and the training data are compared in order to get the best match or no match.

Sign in / Sign up

Export Citation Format

Share Document