Spent fuel monitoring and characterization has been central to safeguards and nuclear facility monitoring for many years. The Digital Cerenkov Viewing Device (DCVD) has been used since the 1980s as a method of defect detection in spent fuel. In recent years, the accounting for large quantities of spent fuel before storage has renewed interest in this relatively quick and inexpensive method. This has an impact not only in safeguards, but also for nuclear power facilities, as accounting can be a long, arduous, and costly process. Additionally, the DCVD demonstrates limited accuracy in more complex cases such as substitution of a fuel rod with steel or a partial defect detection. A second method, gamma emission tomography (GET) has been explored as an improved defect detection method, but is much more expensive and invasive than DCVD. The present investigation identifies deficiencies in both methods and proposes a combination of data gathered from each method to address these deficiencies for improved spent fuel characterization. Initial results are promising, showing 97% detection of a single missing fuel rod when the data types are combined, versus approximately 50% and 70%, respectively, for DCVD and GET data on their own. These classification results are obtained with algorithms derived from facial recognition and applied to this problem, yielding unique accuracy in near real time while also maintaining the information barrier between output and measurement desired in safeguards.