Biometric recognition systems use measures from the body itself to determine the identity of an individual. The electrocardiogram (ECG) has been increasingly used as a biometric measure for person identification, as it is an easily measurable characteristic of all individuals. Our method for ECG acquisition follows an off-the-person approach, using a single ECG lead with non-gelled electrodes placed at the hands. However, this signal is noisier than typical ECG signals acquired on the chest, making subsequent processing more difficult. Therefore, we investigate the applicability of the Wavelet Transform (WT), which decomposes a signal into a time-scale representation according to a given mother wavelet. We use this representation to both segment the R wave of the ECG signal, and as the features for the classification step, defining an appropriate distance measure. We test this framework with real data, using various mother wavelets. Our experimental results show the potential of this framework, and that the best mother wavelet for the evaluated context is the rbio5.5.