Mobile devices have become an integral part of our routine activities. Some of the activities involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.). These mobile electronic services (e-Services) typically require a method to securely identify and authenticate a claimed identity. Currently, e-Services typically use a knowledgebased authentication method by demonstrating the knowledge of secret (e.g. password), but it is vulnerable to a number of security attacks, e.g. shoulder spoofing and brute force attacks. To thwart the attacks and to make the authentication method more secure, this paper describes our efforts in investigating the benefits of integrating touch dynamics biometrics, into a PIN-based authentication method. It reports the collection of a comprehensive reference dataset from 150 subjects, the extraction of feature data from the dataset, and the classifications and the use of the feature data to identify a user. Experimental results show that, even when the PIN is exposed, 9 out of 10 impersonation attempts can be successfully detected.