### Related Works  
Convolutional neural networks (CNNs) have found extensive application in various domains, including traffic sign detection [13], indoor object detection [14], and numerous other fields [15]. The challenge of recognizing faces has attracted interest across diverse disciplines such as psychology, model identification, computer vision, and computer graphics, resulting in a vast and diverse literature on face recognition.  
A hybrid system for face recognition was proposed in [16], which combines a Logistic Regression Classifier (LRC) with a CNN. The approach involves training the CNN to localize and identify faces in images, with subsequent classification of the learned features by the LRC. Experimental results on the Yale face dataset [17] demonstrated improved classification accuracy and reduced processing time. In [18], a CNN-based face identification system with nine layers was introduced, achieving higher recognition rates compared to traditional machine learning and handcrafted feature methods for face identification on the ORL face [15] and AR face datasets [15].  
In [19], a deep learning algorithm for face recognition based on the OpenFace project and utilizing the FaceNet neural network architecture [20] was detailed. The study demonstrated the effectiveness of the incremental learning algorithm in improving performance. The authors of [21] proposed an Active Face Recognition system (AcFR) that uses a CNN to mimic human behavior in common face recognition scenarios. Additionally, ref. [22] introduced a novel face recognition system that uses a deep C2D-CNN model at the decision level.  
Beyond face recognition, CNNs have also been successfully applied in various biomedical fields, such as medical image analysis. In [23], CNNs were used to classify and segment medical images, leading to improved diagnostic accuracy. Another study [24], demonstrated the use of CNNs in predicting disease progression from medical imaging data.  
In the industrial sector, CNNs have been employed for quality control and defect detection. For instance, Ref. [25] explored the application of CNNs in detecting manufacturing defects in assembly lines, while and ref. [26] focused on using CNNs for predictive maintenance by analyzing machinery data.  
These examples illustrate the versatility and effectiveness of CNNs across a broad spectrum of applications, from face recognition to medical diagnostics and industrial automation, highlighting their significant impact in advancing technology and improving efficiency in various domains.  
In this study, we focus on the use of CNNs combined with various filters and data augmentation techniques. Additionally, we employ image-masking techniques to isolate the region of interest for analysis. Furthermore, we incorporate image preprocessing using color filters before feeding the images into the CNN. This comprehensive approach aims to improve the accuracy, effectiveness, and execution time of our classification tasks. We propose a classification method to distinguish between yellow wing (C1), white wing (C2), yellow thigh (C3), and white thigh (C4) using a combination of CNNs and various filters(HOG, LBP, and saliency). This method is based on the idea that feature extraction from the matrix of filters (HOG, LBP, and saliency) using CNNs yields superior results.  
To achieve the established objectives, this document is structured as follows. Section 2 introduces the CNN algorithm, detailing its fundamental principles and functionalities. Section 3 describes the filters for feature extraction. Section 4 describes the materials used and the methodology applied to chicken classification. Section 5 presents a detailed analysis of the results obtained and a discussion, followed by the conclusions in Section 6.