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This paper presents a neuromorphic person re-identification (NPReId) framework to establish the correspondence among individuals observed across two disjoint camera views. The proposed framework comprises three modules (observation, cognition, and contemplation), inspired by the form-and-color-and-depth (FACADE) theory model of object recognition system. In the observation module, a semantic partitioning scheme is introduced to segment a pedestrian into several logical parts, and an exhaustive set of experiments have been carried out to select the best possible complementary feature cues. In the cognition module, an unsupervised procedure is suggested to partition the gallery candidates into multiple consensus clusters with high intra-cluster and low inter-cluster similarity. A supervised classifier is then deployed to learn the relationship between each gallery candidate and its associated cluster …