With the increase in the crime rate, it becomes difficult for police officials to catch criminals. Face recognition is a technique that helps police officials to catch suspects easily with the help of CCTV footage. The constant checking of surveillance video is a tiresome approach that involves substantial visual consideration and is not mentally captivating, making it increasingly inclined to errors. Therefore, the authors proposed an automated criminal recognition system in the present paper. The present paper comprised three phases; the initial phase investigated the performance of various existing face detection algorithms. The second phase proposed a rectified fine-tuned DenseNet169 model for face recognition. In the third phase, the authors proposed an automated criminal recognition system in which the identification of crime-prone areas is made, based on the data collected where most of the criminals are identified. The proposed rectified model outperformed 1.02% to DeepID, 1% to deep convolutional neural network (CNN), and 1.05% to self-learning CNN on the LFW dataset, 1.09% to VGGFace2, 1.13% to SphereFace, and 1.03% to lightweight CNN on the CPLFW dataset, and achieved 88.7% recognition accuracy on a self-created dataset by meticulously selecting the hyperparameters and customizing the layers of the model.