過往的生物識別安全文獻當中，多半是使用批量學習方法來訓練驗證系統。本研究中，採用線上學習方式來提供了一種新穎的方法，為提出的系統隨時間推移建構用戶模型。線上學習方式不僅較符合現實世界的情況，還能提供比批量學習更快的訓練時間。實驗結果顯示，線上學習方式比過去在這一領域的其他方法快75％。線上學習方式的另一個好處是該模型能夠適應使用者行為變化。實驗結果顯示，線上學習方式的EER值較其他不會更新的模型較低，線上學習方法也讓模型隨時間推移改進，現在時間點的註冊者模型的EER值相較於之前時間點更新的模型來的更低。本研究也對我們提出的線上學習方法和與批量學習方法使用盒鬚圖統計分析方法進行比較效能。根據實驗結果顯示，本研究提出的方法之第一四分位數優於批次學習，而中位數接近於批量學習;The traditional authentication mechanisms such as Personal Identification Number (PIN) and password had proven to be insecure as they can be cracked or duplicated easily. To address this issue, the advancement of research on digital security mechanism has seen an increasing interest on the implementation of biometric security system. The objective of biometric-based authentication mechanism is to utilize either physical or behavioral feature to authenticate the genuine users. Even though most biometric authentication systems use physical data as the feature, the physical feature is still prone to the spoofing attack. In this work, a smart-phone authentication system that uses biometric data, specifically behavioral data, to authenticate user is proposed. Using behavioral data as a feature gives the benefit that the authentication key will adapt to the user characteristic so the users do not need to remember the authentication key. The behavioral feature is also difficult to be spoofed and imitated. Moreover, the proposed authentication system is designed based on the non-intrusive approach. The proposed non-intrusive authentication mechanism offers a better solution as the system monitors user′s behavior in the background.
Generally, most researches in the biometric security mechanism use batch learning approach to train authentication system. In this work, online learning approach is adopted to offer a novel method to build the user model over a period of time. Not only mimics the real world situations, online learning approach offers a faster training time when it is compared to batch learning. In the experiment results, the online learning approach has an around 75\% faster training time compared with the previous works in this field.
Another benefit of online learning approach is the capability of the model to be adaptive to user behavior change. In the experiment results, the online learning approach shows lower Equal Error Rate (EER) than the model that is never updated. The online learning approach also shows the model improvement over period of time. Comparing the result of the updated models in online learning approach over a period of time, the current user model has a lower EER than the previously updated model. In this work, the performance of proposed online learning approach is also compared to batch learning approach using box plot statistical analysis. From the experiment results, it showed that the first quartile of proposed work is better than the batch learning and the proposed work has median value near to the result of batch learning.
