Discriminative feature embedding is about vital appreciation within the research area of deep face identification. During this paper, we would suggest a remaining attention based convolutional neural network (ResNet) because differs from facial characteristic implanting, who objectives in conformity with locate outdoors the long-range dependencies regarding rear images through reducing the knowledge redundancy amongst channels and as specialize of the important informative factors on spatial feature maps (SFM). More specifically, the proposed interest module consists about self channel attention (SCA) block then self spatial attention (SSA) barrier who adaptively aggregates the characteristic maps within each duct and spatial domains after find outdoors the inter-channel affinity form yet consequently the interspatial kindred matrix, and then matrix multiplications are conducted because a refined yet robust back feature. With the sight module we proposed, we wish make grade convolutional neural networks (CNNs), like ResNet-50 have greater discriminative government for deep face recognition. The experiments on labelled faces of SMIC-HS, SMIC-NIR, SMIC-VIS, CASME II and CASME I are show that our developed ResNet shape constantly outperforms naive CNNs and achieved the state-of-the-art performance. The proposed work on these micro-expression datasets yields better results with a state-of-the-art accuracy of quite 98% in each dataset. For verification purposes, the videos collected real-time manually from different gender people were tested and an accuracy score of 99.88% and an F1_score of 99.88% was achieved. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.