Pengenalan Wajah Menggunakan Implementasi T-shape Mask Pada Two Dimentional Linear Discriminant Analysis Dan Support Vector Machine

. Face recognition is the identification process to recognize a person\u27s face. Many studies have been developing face recognition methods, one of which is the Two Dimensional Linear Discriminant Analysis (TDLDA) which has pretty good accuracy results with the method of classification Support Vector Machine (SVM). With more training data can add computational time. TDLDA using all the piksel image as input to be processed for feature extraction. Though not all the objects in the area of the face is a significant feature in face recognition. In this study, the proposed use of the T-shape with only use a significant part is the eyes, nose, and mouth are integrated with TDLDA and SVM. The result could reduce computing time on face recognition 21.56% faster than TDLDA method. The accuracy of the results in this study was 91% -96% which is close to the level of accuracy without using a mask on the face

SURVEILLANCE CAMERA UNTUK ABSENSI WAJAH DENGAN BERBASIS TELEGRAM BOT

Abstrak. Penerapan teknologi informasi telah meluas ke berbagai bidang termasuk pendidikan. Di bidang pendidikan, ada banyak penelitian tentang Implementasi teknologi informasi, mulai dari teknologi itu sendiri hingga dampak pengguna dan masyarakat. Sistem kehadiran biometrik adalah salah satu implementasi teknologi yang telah diterapkan di beberapa lembaga pemerintah dan swasta. Sistem kehadiran biometrik selain menggunakan sidik jari juga bisa menggunakan wajah. Sistem kehadiran menggunakan wajah membutuhkan perangkat keras sederhana seperti kamera digital dan sistem terintegrasi. Kehadiran berbasis wajah biasanya menggunakan kamera yang langsung melekat pada perangkat kehadiran, dan wajah harus langsung di depan kamera. Sistem pendeteksian wajah ditambahkan untuk mendukung proses awal dalam pengenalan wajah. Kehadiran menggunakan surveillance camera ini akan diuji di ruang belajar di Universitas Pembangunan Nasional "Veteran" Jawa Timur, dan akan dianalisis tentang keakuratan dan ketepatan sistem. Berdasarkan percobaan yang telah dilakukan, kinerja pengenalan wajah adalah 72,40%. Akurasi ini termasuk dalam kategori sedang. Hasil ini masih belum optimal. Selain itu absensi ini akan terhubung langsung dengan nomor telegram orang tua mahasiswa. Dengan adanya sistem ini diharapkan orangtua mahasiswa dapat memantau kehadiran putra putrinya dalam mengikuti perkuliahan.  Kata Kunci: Deteksi wajah, Absensi, Surveillance Camera, Telegram Bot. DOI : https://doi.org/10.33005/scan.v13i3.144

Adaptive hybrid technique for face recognition

One of the most important biometric features for personal identification is the face. In current paper, a new method of face verification upon on singular value decomposition (SVD) and standard deviation (SD) would be described. Due to many variations in real-life such as pose, illumination, or facial expression, there would be difficulty of face recognition. It should be mentioned that there are many approaches for face recognition, however, there is no one could be considered as the most suitable for many situations. One of the methods used is Singular value vector for an image detecting, but the drawback of this approach is the low rate of recognition, where one scale singular value vector is used for face acknowledgment. There an algorithm has been developed to expand the rate of the recognition. In this paper, an approach has been proposed to associate two feature sets obtained from SVD and SD method. It has noticed a good recognition rate could be obtained from the experimental results, where approximately more that 97.5% recognition rate has obtained on the ORL data base. The results from current proposed method have matched with some techniques and it has shown that this method is better than the existing approaches. An extensive experiment has demonstrated not only better performance, but it offers a great likely to achieve equivalent performance to other categories of state-of-the-art methods

Pengenalan Wajah Menggunakan Implementasi T-shape Mask pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine

Abstract. Face recognition is the identification process to recognize a person's face. Many studies have been developing face recognition methods, one of which is the Two Dimensional Linear Discriminant Analysis (TDLDA) which has pretty good accuracy results with the method of classification Support Vector Machine (SVM). With more training data can add computational time. TDLDA using all the piksel image as input to be processed for feature extraction. Though not all the objects in the area of the face is a significant feature in face recognition. In this study, the proposed use of the T-shape with only use a significant part is the eyes, nose, and mouth are integrated with TDLDA and SVM. The result could reduce computing time on face recognition 21.56% faster than TDLDA method. The accuracy of the results in this study was 91% -96% which is close to the level of accuracy without using a mask on the face.Keyword: face recognition, T-shape, TDLDA, Support vector machine. Abstrak. Pengenalan wajah merupakan proses identifikasi untuk mengenali wajah seseorang. Telah Banyak penelitian yang mengembangkan metode pengenalan wajah, salah satunya adalah Two Dimensional Linear Discriminant Analysis (TDLDA) yang memiliki hasil akurasi yang cukup baik dengan metode klasifikasi Support Vector Machine (SVM). Dengan semakin banyak data training dapat menambah waktu komputasinya. TDLDA menggunakan semua piksel citra sebagai masukan yang akan diproses untuk ekstrasi fitur. Padahal tidak semua objek pada area wajah merupakan fitur yang signifikan dalam pengenalan wajah. Dalam penelitian ini diusulkan penggunaan T-shape dengan hanya menyimpan bagian yang signifikan yaitu mata, hidung, dan mulut yang diintegrasikan dengan TDLDA dan SVM. Hasilnya dapat mengurangi waktu komputasi pada pengenalan wajah 21,56% lebih cepat daripada metode TDLDA. Hasil akurasi pada penelitian ini adalah 91%-96% yang mendekati tingkat akurasi tanpa menggunakan mask pada wajah.Kata Kunci: pengenalan wajah, T-shape, TDLDA, Support vector machine