Yazar "Kilic, Safak" seçeneğine göre listele

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    A New Approach for Human Recognition Through Wearable Sensor Signals
    (Springer Heidelberg, 2021) Kilic, Safak; Kaya, Yilmaz; Askerbeyli, Iman
    Recently, subjects such as human recognition (HR), age estimation and gender recognition have been among the most investigated human-computer interaction topics, in both the academic and other fields. HR is a process in which a person is detected based on the obtained biometrical features. In this study, a new feature extraction method has been suggested through using the signals received from the sensors of the accelerometer, magnetometer and gyroscope attached to the 5 areas on the human body. The feature extraction from the signals is one of the most crucial stage. The reason behind the success of HR is based on the extracted features. However, the extraction of appropriate features for HR is a challenging issue. Various transformation methods like 1D-LBP and 1D-FbLBP have been applied to the sensor-based signals. Following the transformation process, the statistical features have been acquired from the newly developed signals. The classification processes have been carried out with the distinctive methods concerning machine learning (Knn, RF, A1DE, A2DE and ANN) by using these features. According to these results, 1D-LBP (88.4649%) and 1D-FbLBP (91.8281%) methods have been chosen to provide effective features for HR.
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    Öğe
    Deep Learning Based Gender Identification Using Ear Images
    (Int Information & Engineering Technology Assoc, 2023) Kilic, Safak; Dogan, Yahya
    The classification of an individual as male or female is a significant issue with several practical implications. In recent years, automatic gender identification has garnered considerable interest because of its potential applications in e-commerce and the accumulation of demographic data. Recent observations indicate that models based on deep learning have attained remarkable success in a variety of problem domains. In this study, our aim is to establish an end-to-end model that capitalizes on the strengths of competing convolutional neural network (CNN) and vision transformer (ViT) models. To accomplish this, we propose a novel approach that combines the MobileNetV2 model, which is recognized for having fewer parameters than other CNN models, with the ViT model. Through rigorous evaluations, we have compared our proposed model with other recent studies using the accuracy metric. Our model attained state-of-the-art performance with a remarkable score of 96.66% on the EarVN1.0 dataset, yielding impressive results. In addition, we provide t-SNE results that demonstrate our model's superior learning representation. Notably, the results show a more effective disentanglement of classes.
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    Öğe
    Using ResNet Transfer Deep Learning Methods in Person Identification According to Physical Actions
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Kilic, Safak; Askerzade, Iman; Kaya, Yilmaz
    Today, biometric technologies are one of the areas of information security which are increasingly used in all areas required by human security. The subjects such as person identification (PI), age prediction, and gender recognition are among the topics of human-computer interactivity that have been commonly researched in both academic and other areas in recent years. PI is the process of identifying the person according to biometric features obtained. In this study, the PI process was carried out with ResNet transfer deep learning methods by using the signals from an accelerometer, magnetometer and gyroscope sensors attached to 5 different regions of the persons. Here, the persons were identified depending on different physical actions and effective actions in the PI were determined. Furthermore, the effective body areas have also been identified in PI. Generally, high success rates have been observed through ResNet architecture. This study has shown that the signals of wearable accelerometer, gyroscope, magnetometer sensors can be used as a new biometric system to prevent identity fraud attacks. In summary, the proposed method can be greatly beneficial for the effective use of wearable sensor signals in biometric applications.