Fingerprint dataset is one of the most broadly implemented and broadcasted biometrics for the derivation of individual feature identification. Fingerprint dataset performs in multiple approaches, such as applying query by image content techniques, reviewing criminal offenders, surveillance, taking a difficult decision, searching immediately, and anthropological research because of the uniqueness and persistence of the fingerprint dataset. Here in this research signifies an efficient way of identifying two key biological features: blood group and gender distinguish, based on the fingerprint dataset, applying Deep Convolutional Neural Networks (D-CNNs). The proposed model contains a modified approach of D-CNN and is trained and developed on a self-built fingerprint dataset. Thus, the algorithm applied here aims to observe how prominent the model performs for the custom-built dataset. The proposed model of D-CNN approach proved to be an improved technique and reaches an accuracy of around 99.968% based on the fingerprint images by the individuals for the identification of blood group and gender.