## 1 Introduction  
Face recognition systems played an important role in the human verification process to eliminate unauthorized user access in various applications. The users are verified with the help of an ID verification process in which the user's facial features are stored in the database to complete the user authentication. Facial identification enhances overall security in applications such as e-banking, e-commerce, forensics, airport security, etc. [1, 2]. Face recognition aims to give a computer system the ability to quickly and precisely recognize human faces in images or videos [3, 4]. Numerous algorithms and methods, including recently proposed deep learning models, have been proposed to improve face recognition performance [5, 6, 7]. However, the face recognition system is far from perfect in terms of accuracy.  
Meanwhile, the environment in which face recognition is used influences its accuracy. Various factors influence face recognition accuracy, particularly unconstrained face recognition, because face images exhibit multiple variations. These factors include pose variation, scale variation, partial occlusion, and complex illumination, which may impede recognition accuracies [1; 8].  
Most researchers use different techniques and algorithms to locate facial features. In addition, large-scale identification methods are incorporated to explore the facial features to maximize facial recognition accuracy. Learning ability, variety, and generalization are all advantages of deep neural network-based recognition algorithms [9; 10; 11]. When real-time operation is necessary and in unconstrained situations, efficient algorithms still have significant constraints due to the high accuracy and processing efficiency requirement [2]. Therefore, face recognition remains a considerable challenge in real-time applications, and it is a hot research topic in computer vision, deep learning, real-time systems, and other fields. The study uses the Gabor filter and deep learning model to maximize the facial recognition rate. The Gabor filter analyzes the captured images which are effectively utilized to perform the image textures. This method maximizes interpretability and discrimination tracker performance. In addition, it is able to locate the features related region with minimal computation difficulties. Therefore, this research uses the Gabor filter to extract related textural features from the input image. The extracted features are further investigated with the help of the Stacked Sparse Autoencoder (SSAE) deep neural model to identify the authenticated user. The main intention of this work is to create a robust and flexible system to recognize face images while trying to access the data in the database. The proposed hybrid Gabor filter and deep learning models effectively investigate the facial images, and verification is performed. During the analysis, the system uses the OLR dataset and the Extended Yale-B Face image dataset to evaluate the proposed system's efficiency. The information in the databases was captured with the help of different emotions and directions that help to recognize the user's facial expression with a minimum false acceptance rate. These databases were captured using excellent mobile and multimedia technology environments. This leads to an effective image dataset with a huge volume of images. These images are more helpful in evaluating the produced SSAE system's performance in order to achieve the stated research objectives. Hence, this study uses the hybridized deep neural model (SSAE) to maximize face recognition accuracy.  
The rest of the manuscript is arranged as follows: Section 2 describes the materials and methods adopted in this work to achieve the stated objectives. Section 3 presents the experimental results and discussion, followed by an evaluation of the proposed hybrid method of the Gabor filter and SSAE system's efficiency. Section 4 describes some important research findings in detail. Section 5 concludes the paper and discusses the future direction of these works.