Biometric feature-based personal identification is gaining popularity these days since it is more trustworthy than previous approaches and has a wide range of applications. Furthermore, hand-based biometrics have better user acceptability, making finger knuckles a relatively acceptable trait due to their location and the added benefit of being less susceptible to harm. Finger knuckle prints are the inherent skin patterns that form at the knuckles of the back of the hand and have been proven to be extremely rich in textures, hence they may be utilized to uniquely identify a person. Our study seeks to explore the innate advantage of finger knuckles and the benefit of combining multiple features from knuckle areas, minor and major, using symmetric sum algorithms to fuse scores from each of the traits. The proposed methodology is tested on a modified AlexNet model as well as the original AlexNet, modified ResNet50, binarized statistical image features, and principal component analysis for comparison. Experimental results are obtained using PolyUKnuckleV1 finger knuckle datasets provided by Hong Kong Polytechnic University. The results reinforced the idea that multimodal biometric systems are stronger and that finger knuckles are unique to each person.