QBGA–SVM for Face Recognition of Livable Cities

Abstract

With the continuous expansion of urbanization, the problem of human settlements has become increasingly prominent. Green, economical, intelligent and livable cities have become the urgent needs of future urban planning. The evaluation of urban livability is not only one of the judgment criteria of urban competitiveness, but also an important factor affecting the speed of urban development. Among them, the safety factor of the city is the important guarantee of other aspects, so this paper intends to design a high-precision face recognition algorithm to make efforts for the safety construction of livable cities. Aiming at the shortcomings of the standard support vector machine (SVM), combined with the quantum-behaved mechanism, a quantum-behaved genetic algorithm–SVM (QBGA–SVM) is proposed in the paper. The experimental results for the human face databases show that QBGA–SVM is superior to the comparison algorithms in both accuracy and stability. Finally, QBGA–SVM is applied to face images of the real world, and the results are better than the other algorithms.