Crime scene suspect prediction involves classifying potential perpetrators based on location, time, and crime type. Electronic forensics in large data scenarios poses challenges for investigators. Law enforcement relies on hand-drawn or computer-generated face sketches for perpetrator identification. To aid recognition, an automated method for face sketch expansion is essential. Deep learning models, like the Golden jackal optimized artificial neural network (GJO-ANN), generate face sketches for crime detection. Comparing these sketches with eyewitness and artist renditions helps identify similarities, enabling perpetrator detection. Experimental results validate the superior performance of GJO-ANN in face sketch synthesis for crime detection.

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