Deteksi Wanita Berhijab dan tidak Berhijab dengan menggunakan Metode Mask RCNN

Setiap santriwati yang tinggal di pesantren wajib menggunakan hijab. Untuk melakukan control dan monitoring penggunaan hijab di pesantren saat ini masih dilakukan secara manual oleh pihak keamanan. Proses control dan monitoring yang dilakukan secara manual ini membutuhkan waktu dan proses yang lama serta membutuhkan sumber daya manusia yang banyak. Untuk membantu mengatasi permasalahan yang ada, maka dibutuhkan sistem yang dapat memonitoring pemakaian hijab secara otomatis. Pada penelitian ini diusulkan menggunakan metode MASK RCNN untuk mendeteksi objek wanita yang tidak berhijab dan wanita yang berhijab dari gambar digital. Dataset yang digunakan pada penelitian ini terdapat 3 kategori yaitu wanita berhijab syar’i, wanita berhijab tidak syar’i, dan wanita tidak berhijab yang memiliki 4 class yaitu wajah, rambut, hijab syar’i, hijab non syar’i. Proses yang dilakukan pada metode tersebut terdapat 2 tahapan yaitu data training dan data testing. Data training yang digunakan adalah 1500 citra digital setiap kategori berjumlah 500 citra digital dan data testing yaitu digunakan 150 gambar setiap kategori berjumlah 50 gambar. Model ini dilatih dengan metode MASK RCNN data training memperoleh epoch 30 dengan nilai loss 0,1770, nilai val_loss 0,1745 dan waktu 473s 946ms/step. Hasil eksperimen menunjukkan bahwa metode yang diusulkan dapat mendeteki hijab syar’i dengan tingkat akurasi 96%, hijab tidak syar’i dengan tingkat akurasi 96 % dan tidak berhijab dengan tingkat akurasi 94%

Real time ear recognition using deep learning

Automatic identity recognition of ear images represents an active area of interest within the biometric community. The human ear is a perfect source of data for passive person identification. Ear images can be captured from a distance and in a covert manner; this makes ear recognition technology an attractive choice for security applications and surveillance in addition to related application domains. Differing from other biometric modalities, the human ear is neither affected by expressions like faces are nor do need closer touching like fingerprints do. In this paper, a deep learning object detector called faster region based convolutional neural networks (Faster R-CNN) is used for ear detection. A convolutional neural network (CNN) is used as feature extraction. principal component analysis (PCA) and genetic algorithm are used for feature reduction and selection respectively and a fully connected artificial neural network as a matcher. The testing proved the accuracy of 97.8% percentage of success with acceptable speed and it confirmed the accuracy and robustness of the proposed system

Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation

The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.Comment: Accepted into ICCBS 202

Analysis of 2D and 3D images of the human head for shape, expression and gaze

Analysis of the full human head in the context of computer vision has been an ongoing research area for years. While the deep learning community has witnessed the trend of constructing end-to-end models that solve the problem in one pass, it is challenging to apply such a procedure to full human heads. This is because human heads are complicated and have numerous relatively small components with high-frequency details. For example, in a high-quality 3D scan of a full human head from the Headspace dataset, each ear part only occupies 1.5\% of the total vertices. A method that aims to reconstruct full 3D heads in an end-to-end manner is prone to ignoring the detail of ears. Therefore, this thesis focuses on the analysis of small components of the full human head individually but approaches each in an end-to-end training manner. The details of these three main contributions of the three individual parts are presented in three separate chapters. The first contribution aims at reconstructing the underlying 3D ear geometry and colour details given a monocular RGB image and uses the geometry information to initialise a model-fitting process that finds 55 3D ear landmarks on raw 3D head scans. The second contribution employs a similar pipeline but applies it to an eye-region and eyeball model. The work focuses on building a method that has the advantages of both the model-based approach and the appearance-based approach, resulting in an explicit model with state-of-the-art gaze prediction precision. The final work focuses on the separation of the facial identity and the facial expression via learning a disentangled representation. We design an autoencoder that extracts facial identity and facial expression representations separately. Finally, we overview our contributions and the prospects of the future applications that are enabled by them