AbstractMost fMRI inferences are based on analyzing the scans of a cohort. Thus, the individual variability of a subject is often overlooked in these studies. Recently, there has been a growing interest in individual differences in brain connectivity also known as individual connectome. Various studies have demonstrated the individual specific component of functional connectivity (FC), which has enormous potential to identify participants across consecutive testing sessions. Many machine learning and dictionary learning‐based approaches have been used to extract these subject‐specific components either from the blood oxygen level dependent (BOLD) signal or from the FC. In addition, several studies have reported that some resting‐state networks have more individual‐specific information than others. This study compares four different dictionary‐learning algorithms that compute the individual variability from the network‐specific FC computed from resting‐state functional Magnetic Resonance Imaging (rs‐fMRI) data having 10 scans per subject. The study also compares the effect of two FC normalization techniques, namely, Fisher Z normalization and degree normalization on the extracted subject‐specific components. To quantitatively evaluate the extracted subject‐specific component, a metric namedOverlapis proposed, and it is used in combination with the existing differential identifiabilityIdiffmetric. It is based on the hypothesis that the subject‐specific FC vectors should be similar within the same subject and different across different subjects. Results indicate that Fisher Z transformed subject‐specific fronto‐parietal and default mode network extracted using Common Orthogonal Basis Extraction (COBE) dictionary learning have the best features to identify a participant.