Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah Nawala IAES dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar tentang perkembangan teknologi face recognition. Face recognition adalah teknologi biometrik yang melibatkan identifikasi dan verifikasi identitas seseorang berdasarkan fitur wajah mereka. Teknologi ini merupakan bagian dari metode autentikasi biometrik dan tengah hype serta praktis untuk digunakan dalam beberapa tahun terakhir. Penting untuk dicatat bahwa meskipun teknologi face recognition menawarkan banyak manfaat, penerapannya harus dilakukan dengan hati-hati, dengan pertimbangan privasi dan akurasi. Bazatbekov dkk. (2023) melakukan penelitian untuk meningkatkan akurasi menggunakan principal component analysis (PCA), triplet similarity embedding, dan projection. Lebih detail terkait penelitian tersebut dapat dilihat pada atikel berikut:
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.
Implementasi teknologi face recognition telah mendukung perkembangan bidang-bidang lain, seperti pada bidang property dan medis. Hutomo dan Wicaksono (2022) mengimplementasikan face recognition pada bidang property untuk mengamankan sebuah bangunan. Mereka membuat kunci pintu otomatis yang dapat mendeteksi wajah, sehingga pintu tidak memerlukan kontak fisik untuk membukanya. Adapun penjelasan lebih lengkap dapat dilihat pada artikel berikut:
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.
Di bidang medis, face recognition dapat digunakan untuk mengidentifikasi penyakit yang diderita oleh pasien. Meskipun tidak secara terperinci mengenai penyakit yang diderita, namun hal tersebut dapat menjadi early warning untuk memeriksa lebih dalam terkait penyakit tersebut. Aurellia dan Rahman (2023) mengkombinasikan face recognition dan kecerdasan buatan untuk mengklasifikasi penyakit yang terdeteksi berdasarkan wajah yang diinputkan, kemudian menganalisis data tersebut berdasarkan data yang ada pada basisdata. Selengkapnya terkait penelitian tersebut dapat diakses pada laman berikut:
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.
Di kepolisian, face recognition menjadi salah satu alat untuk mempermudah sebuah investigasi. Berbekal dengan wajah yang disimpan untuk pencarian, kemudian mencarinya melalui kamera maupun video, dapat melacak dimana orang pemilik wajah tersebut berada. Lakshmi dan Arakeri (2023) mengembangkan metode ini, hanya dengan berbekal sketsa wajah dari kriminal yang dicari dapat mengidentifikasi wajah pada 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.
Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai perkembangan face recognition. Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman dan membaca artikel secara GRATIS melalui tautan-tautan berikut: https://beei.org/, https://ijra.iaescore.com/, https://ijai.iaescore.com/, dan https://ijece.iaescore.com/.
Redaksi: I. Busthomi