Biometric authentication is a common way of granting access to a system or device. The ear, like fingerprints, retina, iris, face, voice, and so on, is a biometric modality. Compared to other biometric organs, the anatomy of a human’s ear remains stable from birth to old life. As a visible organ with an easily acquired image, it may also be a source of a biometric signature that may be used to identify individuals. This research demonstrates two approaches to recognizing a person from 2D ear images: non-deep MLmodels and deep learning-based ML models. The first, or classic, model investigates computer vision preprocessing techniques suchas converting an RGB image to monochrome, then rescaling and locating the entropy. The key weighted characteristics from the earimages were extracted using Independent Component Analysis (ICA) and Principal Component Analysis (PCA). A Gaussian ProcessClassifier (GPC) is then utilized for classification and several kernels such as RBF, Rational Quadratic, and Matern. In the secondtechnique, a deep learning-based ML model called You Only Look Once (YOLO) is utilized to categorize the ear images and identify the source individual without preprocessing. We gathered a standard ear dataset (EarVN1.0 Dataset) from 164 people, totaling 27,592 training images. For testing reasons, 820 images were chosen randomly, five images from each of 164 people. The models were built on the Google Colaboratory server using the Python language framework and GPU-based implementation on the Jupyter Notebook.