Hand Gesture Based Biometric Authentication Using Machine Learning Approaches

Öz

Authentication is becoming an important task in the field of modern technology. It is a process that allows a device to confirm that it recognizes a user interacting with a system entity. In this study, a single-handed gesture-based user authentication using the Leap Motion (LM) device has been investigated. The study consists of hand gesture tracking, acquisition of image frames, preprocessing, feature extraction and selection, dimension reduction, classification, and validation steps. A dataset was collected by 40 users by determining 85 features prepared by considering the similarities and differences of hand biometrics. Comparison analyses were performed by applying the Pearson Correlation Coefficient (PCC) feature selection algorithm and Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) dimensionality reduction methods to this dataset. The verification performance was tested with different machine learning algorithm methods. A 5-fold cross-validation method was used to test the validity of this proposed system and the accuracy of the obtained results. In biometric authentication, the best result was obtained with the kernel-based extreme learning machine (K-ELM) approach with a rate of 96.50%. When the applications were examined in all stages, it was observed that the K-ELM classifier maintained its performance rates and gave the highest performance rate. At the same time, it was seen that the K-ELM classifier has a stable structure.

Anahtar Kelimeler

• Biometrics
• authentication
• hand gesture
• machine learning
• feature extraction

Makine Öğrenimi Yaklaşımları Kullanarak El Hareketi Tabanlı Biyometrik Kimlik Doğrulama

Öz

Kimlik doğrulama, modern teknoloji alanında önemli bir görev haline gelmektedir. Bu, bir cihazın bir sistem varlığıyla etkileşimde bulunan bir kullanıcıyı tanıdığını onaylamasına olanak tanıyan bir süreçtir. Bu çalışmada, Leap Motion (LM) cihazı kullanılarak tek elle gerçekleştirilen hareket bazlı kullanıcı kimlik doğrulama üzerine çalışılmıştır. Çalışma el hareketi takip, görüntü karelerinin elde edilmesi, önişlem, öznitelik çıkarımı ve seçimi, boyut indirgeme, sınıflandırma ve doğrulama adımlarından oluşmaktadır. El biyometrisinin benzerlik ve farklılıkları dikkate alınarak hazırlanan 85 özellik belirlenerek 40 kullanıcı tarafından veri kümesi oluşturulmuştur. Bu veri kümesine Pearson Korelasyon Katsayısı (PCC) özellik seçim algoritması ile Doğrusal Ayırım Analizi (LDA) ve Temel Bileşenler Analizi (PCA) boyut indirgeme yöntemleri uygulanarak karşılaştırma analizleri yapılmıştır. Doğrulama başarımı farklı makine öğrenimi algoritma yöntemleri ile test edilmiştir. Önerilen bu sistemin geçerliliği ve elde edilen sonuçların doğruluğunu test etmek için 5 katlamalı çapraz doğrulama yöntemi kullanılmıştır. Biyometrik kimlik doğrulama, en iyi sonuç %96,50 oranı ile çekirdek tabanlı aşırı öğrenme makinesi (K-ELM) yaklaşımı ile elde edilmiştir. Tüm aşamalarda uygulamalara bakıldığında, K-ELM sınıflandırıcının başarım oranlarını koruduğunu ve en yüksek başarım oranını verdiği gözlemlenmiştir. Aynı zamanda K-ELM sınıflandırıcının kararlı bir yapıda olduğu görülmüştür.

Anahtar Kelimeler

• Biyometrik
• kimlik doğrulama
• el hareketi
• makine öğrenimi
• özellik çıkarımı

Kaynakça

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