Least error rate is achieved by the proposed technique with maximum feature reduction in minimum time for all the standard databases. A comparison of the proposed technique with already existing/reported techniques for the similar applications has been provided. The technique shows significant results for all databases when implemented on Raspberry Pi. The validation of proposed technique is proved on the basis of well-known databases. The proposed technique is suitable for all visual recognition applications deployed in IoT based surveillance devices due to higher dimension reduction. This redundancy is major contributor to memory and time consumption for battery based surveillance systems. The redundant features are discarded by applying the fast subspace decomposition over the Gaussian distributed Local Binary Pattern (LBP) features. The proposed technique extracts the features for visual recognition using local binary pattern histogram. In this manuscript, efficient fast subspace decomposition over Chi Square transformation is proposed for IoT based on smart city surveillance systems. For these real time applications in smart cities, efficient visual recognition systems are need of the hour. In such devices, automatic face recognition requires a low powered memory efficient visual computing system. Smart city surveillance systems are the battery operated light weight Internet of Things (IoT) devices.
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