Identity verification is becoming more and more crucial in a variety of real-world applications, including identity checks at airports, apartment door locks, and cell phones. This process requires a methodology that is fast, precise, scalable to accommodate additional users, and adaptable to variations in face angle, brightness, and other variables. In order to address the aforementioned difficulties, we provide four facial recognition techniques in this work. First, the CNN architecture was presented. Then, We developed the CNN Decoder for face encoding to improve model accuracy and overcome the difficulty of retraining the model when adding new people. Next, we presented two capsule network topologies to address face angle-related problems. The COMSATS Face Dataset is the dataset that we used in our study for testing, training, and assessment. According to an experiment, CNN recognizes faces with 93% accuracy, the decoder with 99% accuracy, the CapsNet with CNN 81% accuracy, and the CapsNet with VGG-19 with 99% accuracy. The latter is thought to yield the greatest results when it comes to distinguishing faces from various viewing angles.