scholarly journals Development of a personal identification technique for automation systems

2021 ◽  
Vol 1047 (1) ◽  
pp. 012138
Author(s):  
V A Chastikova ◽  
S A Zherlitsyn ◽  
Y I Volya
Keyword(s):  
Personal Identification ◽  
Automation Systems ◽  
Identification Technique

scholarly journals Real Time Face Recognition based Smart Lab for Energy Conservation

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 32-41
Author(s):  
M. Karthikeyan ◽  
T.S. Subashini ◽  
M.S. Prashanth
Keyword(s):  
Face Recognition ◽  
Object Identification ◽  
Home Automation ◽  
Human Beings ◽  
Environmentally Conscious ◽  
Repetitive Tasks ◽  
Automation Systems ◽  
The Face ◽  
Identification Technique ◽  
Haar Cascade

Home automation offers a good solution to help conserve our natural resources in a time when we are all becoming more environmentally conscious. Home automation systems can reduce power consumption and when they are not in use automatically turn off lights and appliances. With home automation, many repetitive tasks can be performed automatically or with fewer steps. For example, each time the person gets out of his computer desk, for instance, the fan and the lights need to be turned off and switched on when he comes back to the computer desk. This is a repetitive task, and failure to do so leads to a waste of energy. This paper proposes a security/energy saving system based on face recognition to monitor the fan and lights depending on the presence or absence of the authenticated user. Initially, the authenticated faces/users LBPH (Local Binary Pattern Histogram) features were extracted and modelled using SVM to construct the face profile of all authenticated users. The webcam catches the user's picture before the PC and the Haar-cascade classifier, a profound learning object identification technique is used to identify face objects from the background. The facial recognition techniques were implemented with python and linked to the cloud environment of Ada-Fruit in order to enable or disable the light and fan on the desk. The relay status is transmitted from Ada Fruit Cloud to Arduino Esp8266 using the MQTT Protocol. If the unidentified user in the webcam is detected by this device, the information in the cloud will be set to ' off ' status, allowing light and fan to be switched off. Although Passive Infrared Sensor (PIR) is widely used in home automation systems, PIR sensors detect heat traces in a room, so they are not very sensitive when the room itself is hot. Therefore, in some countries such as INDIA, PIR sensors are unable to detect human beings in the summer. This system is an alternative to commonly used PIR sensors in the home automation process.

AN EFFICIENT EIGEN VALUES BASED TECHNIQUE FOR ONLINE IRIS IMAGE COMPRESSION AND IDENTIFICATION

2011 ◽  
Vol 08 (02) ◽  
pp. 133-152 ◽  
Author(s):  
KAMTA NATH MISHRA ◽  
ANUPAM AGRAWAL ◽  
PRAKASH C. SRIVASTAVA ◽  
VIVEK TRIPATHI ◽  
VISHAL GUPTA
Keyword(s):  
Image Compression ◽  
Smart Card ◽  
Image Databases ◽  
Personal Identification ◽  
Identical Twins ◽  
Image Identification ◽  
Iris Image ◽  
Eigen Values ◽  
Identification Technique ◽  
Noise Disturbances

This paper introduces an efficient Eigen values based technique for online iris image compression and identification of a human including the case of identical twins. The iris image is extracted after removing the pupil, eye brow, skin and other noise disturbances from an actual image. The extracted iris image is divided into different blocks of size of 16 × 16. Now, Eigen values are calculated for each block and these Eigen values are stored in the smart card memory for further identification. Therefore, when checking if two iris images are identical or not, all we need is to compare the stored Eigen values with online calculated Eigen values. If two iris images have the same Eigen values, this means that both iris images belong to the same person. In our research, we have concluded that iris images of different persons have different Eigen values, including the case of identical twins. We conducted experiments on CASIA and Multimedia University iris image databases and we found that our Eigen Values Based Iris Image Identification Technique is giving 99.99% accuracy for the same image of identical twins and individuals. The implementation leads us to believe that our method is giving the best matching result in the case of identical twins and individuals. It is an efficient, secure and economically feasible approach for online personal identification.

scholarly journals Novel Distance Metric for Touch less Footprint Based Identification Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 1011-1016
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Personal Identification ◽  
Shape Features ◽  
Distance Metric ◽  
Mahalanobis Distances ◽  
Behavioral Attribute ◽  
Identification Technique ◽  
Pca Method ◽  
Personal Recognition

In this fast-paced technology-driven today's era, biometrics is not the new buzzword in the information security domain. Biometrics uses any physiological or/ and behavioral attribute/s of an individual for personal identification and/or verification. In biometrics, so many traits, like a fingerprint, face, palm, retina, iris, ECG, gait, voice, and signature, etc., have been used from ages to uniquely identify a human being. Biometrics based on Footprints is the latest practice for personal identification. Like fingerprints and palmprints, footprints of individuals carry uniqueness; hence can be used in biometrics for personal recognition. This work investigates the powerfulness of footprints by extracting texture and shape features using Principal Component Analysis (PCA) method based upon Eigenfeet and introduces a new distance metric during the matching phase. Experimental results show that the new distance metric shows better results in comparison to the Euclidean, Manhattan and Mahalanobis distances.

scholarly journals CNN-Based Personal Identification System Using Resting State Electroencephalography

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yongdong Fan ◽  
Xiaoyu Shi ◽  
Qiong Li
Keyword(s):  
Resting State ◽  
Data Augmentation ◽  
Low Cost ◽  
Personal Identification ◽  
Acquisition Time ◽  
Identification System ◽  
Short Acquisition Time ◽  
Average Accuracy ◽  
Biometric Characteristic ◽  
Identification Technique

As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.

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