An efficient clustering-based non-fiducial approach for ECG biometric recognition

Recognition of individuals through different bio-metric traits is becoming increasingly important. Apart from traditional biomarkers (like fingerprints), many alternative traits have been proposed during the last two decades: ECG and EEG signals, iris or facial recognition, behavioral traits, etc. Several works have shown that ECG-based recognition is a feasible alternative for either stand-alone or multibiometric recognition systems. In this paper, we propose a novel, efficient and scalable clustering-based method for ECG biometric recognition. First of all, fixed length segments of the ECG are extracted without the need of computing any fiducial points. Unique traits for each subject are then obtained by computing the autocorrelation (AC) of each segment, followed by the discrete cosine transform (DCT) to compress the available information. Finally, hierarchical ag-glomerative clustering (HAC) is applied to generate the groups that will be used later on for classification. The combination of the DCT to reduce the feature dimensionality and the HAC to decrease the number of features required by the classifier results in an efficient method both from the memory storage and computational point of view. Furthermore, the proposed AC/DCT-HAC (ADH) approach is robust, since no fiducial points (which may be difficult to extract reliably) are required, scalable and attains a better performance than other approaches with higher storage/computational cost.