In this paper, we present two approaches for identification based on biometric gait using acceleration sensor - called accelerometer. In contrast to preceding works, acceleration data are acquired from built-in sensor in mobile phone placed at the trouser pocket position. Data are then analyzed in both time domain and frequency domain. In time domain, gait templates are extracted and Dynamic Time Warping (DTW) is used to evaluate the similarity score. On the other hand, extracted features in frequency domain are classified using Support Vector Machine (SVM). With the participation of total 11 volunteers over 24 years old in our experiment, we achieved the accuracy of both methods respectively 79.1% and 92.7%.