Person identification using behavioural biometrics has been an active area of research. Eye-blink patterns provide a unique set of characteristics that can identify individuals, as they are difficult to imitate. Current blink pattern-based biometric identification approaches use obtrusive head-mounted sensors to accurately capture eye blinks, which limits their daily use. In addition, they rely on one-dimensional temporal signals, such as eye-aspect ratio (EAR), or require fusion with additional biometric data. In contrast, we introduce novel two-dimensional spatio-temporal features, drawing from earlier successful gait recognition templates. In particular, our approach turns eye-blink sequences into discriminative spatiotemporal patterns. We construct and compare two types of “Time Images” to determine the most effective spatio-temporal template: (i) a Colour Outline Time Image (COTI), which uses a red-to-violet hue cycle to convey blink progression; and (ii) a Mean Intensity Time Image (MITI), which is an average intensity image of the periocular region. Our approach uses dynamic features extracted from the EAR and creates superimposed blink frame representations to capture the temporal evolution of eye contours. We used two different Convolutional Neural Network (CNN) architectures to classify these time images. Our method achieves strong results on two publicly available datasets that cover a range of covariates and demonstrates robust identification capability. This research serves as a baseline for future studies and demonstrates the validity of using eye-blink patterns as a standalone biometric feature for person identification.