Greetings, fellow Nawala! May you always be in good health.
This is the IAES Nawala from the Institute of Advanced Engineering and Science. Today we will share news about the development of face recognition technology. Face recognition is a biometric technology that involves identifying and verifying a person’s identity based on their facial features. It is part of the biometric authentication methods and has been gaining hype and practical use in recent years. It is important to note that while face recognition technology offers many benefits, its implementation must be done carefully, with privacy and accuracy in mind. Bazatbekov et al. (2023) conducted research to improve accuracy using principal component analysis (PCA), triplet similarity embedding, and projection. More details regarding the research can be seen in the following article:
2D face recognition using PCA and triplet similarity embedding
Bek Bazatbekov, Cemil Turan, Shirali Kadyrov, Askhat Aitimov
The aim of this study is to propose a new robust face recognition algorithm by combining principal component analysis (PCA), Triplet Similarity Embedding based technique and Projection as a similarity metric at the different stages of the recognition processes. The main idea is to use PCA for feature extraction and dimensionality reduction, then train the triplet similarity embedding to accommodate changes in the facial poses, and finally use orthogonal projection as a similarity metric for classification. We use the open source ORL dataset to conduct the experiments to find the recognition rates of the proposed algorithm and compare them to the performance of one of the very well-known machine learning algorithms k-Nearest Neighbor classifier. Our experimental results show that the proposed model outperforms the kNN. Moreover, when the training set is smaller than the test set, the performance contribution of triplet similarity embedding during the learning phase becomes more visible compared to without it.
The implementation of face recognition technology has supported the development of other fields, such as in the property and medical fields. Hutomo and Wicaksono (2022) implemented face recognition in the property field to secure a building. They made an automatic door lock that can detect faces, so that the door does not require physical contact to open it. A more complete explanation can be seen in the following article:
A smart door prototype with a face recognition capability
Ivan Surya Hutomo, Handy Wicaksono
This research aimed to integrate a face recognition capability in a smart door prototype. By using a camera-based face recognition, the house owner does not need to make physical contact to open the door. Avoid physical contact is important due to the coronavirus disease 2019 (COVID19) pandemic. Raspberry Pi 3B was used as the main controller, while a servo motor was utilized as a locking door actuator. The program was developed using Node-RED, Blynk, and message queue telemetry transport (MQTT) platforms which are very powerful for developing internet of things (IoT) devices. All of the programs were coded using Python. Haar cascade and local binary pattern histogram methods were implemented on the face recognition stage. Google Assistant integration was done by using Dialogflow and Firebase as Google Cloud services. Integration of face recognition and the smart door was successful. The smart door was unlocked if faces were recognized (average threshold=60%). If a face was not recognized, an email notification containing a face image is sent to the house owner. The Google Assistant could handle user requests successfully with a success rate of 92.8% from 147 trials.
In the medical field, face recognition can be used to identify diseases suffered by patients. Although not in detail about the disease suffered, it can be an early warning to examine more deeply related to the disease. Aurellia and Rahman (2023) combine face recognition and artificial intelligence to classify detected diseases based on the inputted face, then analyze the data based on existing data in the database. More information about the research can be accessed on the following page:
Face recognition in identifying genetic diseases: a progress review
Salsabila Aurellia, Siti Fauziyah Rahman
Genetic diseases vary widely. Practitioners often face the complexity of determining genetic diseases. In distinguishing one genetic disease from another, it is difficult to do without a thorough test on the patient or also known as genetic testing. However, in some previous studies, genetic diseases have unique physical characteristics in sufferers. This leads to detecting differences in these physical characteristics to assist doctors in diagnosing people with genetic diseases. In recent years, facial recognition research has been quite active. Researchers continue to develop it from various existing methods, algorithms, approaches, and databases where the application is applied in various fields, one of which is medical imagery. Face recognition is one of the options for identifying disease. The condition of a person’s face can be said to be a representation of a person’s health. Where the accuracy in early detection can be pretty good, so face recognition is also one of the solutions that can be used to identify various genetic diseases in collaboration with artificial intelligence. This article review will focus more on the development of facial recognition in 2-dimensional images, showing that different methods can produce different results and face recognition can also overcome complex genetic disease variations.
In the police, face recognition is one of the tools to facilitate an investigation. Armed with a face that is stored for search, then search it through cameras or videos, can track where the person who owns the face is. Lakshmi and Arakeri (2023) developed this method, armed only with a sketch of the face of the wanted criminal can identify the face in the video.
A novel sketch based face recognition in unconstrained video for criminal investigation
Napa Lakshmi, Megha P. Arakeri
Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.
The above articles are just a small part of the research on the development of face recognition. To get more information, readers can visit the page and read articles for FREE through the following links: https://beei.org/, https://ijra.iaescore.com/, https://ijai.iaescore.com/, and https://ijece.iaescore.com/.