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Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

The Nurse Scheduling Problem (NSP) is a combination of optimization problem and important management functions performed by nurses which directly affected the hospital services and the patient care. The NSP is varied which tends to be solved with nature inspired search approaches such as particle swarm optimization (PSO). Hence, the main purpose of this paper is to utilize the PSO to find the balance of nurse assignment by considering the coverage demand and nurse preferences. The actual task of generating the nurse schedule is investigated in one of the public hospitals in Malaysia. The proposed algorithms are tested on the real data and the obtained results demonstrate that the proposed particle swarm optimization has better performance to cope with complex scheduling problem. The experimental results show that the guided randomly PSO algorithm is more effective in term of the time constraint and the feasible assignment solution.