4. The object recognition apparatus according to claim 3 , wherein in the case where the extraction unit has extracted a first luminance vector I 1m calculated from images of people included in the first dictionary information of the first category and a second luminance vector I 2n calculated from n images of people included in the second dictionary information of the second category, the determination unit uses the following formula to calculate an average similarity between the first category and the second category and determines that the first dictionary information included in the first category and the second dictionary information included in the second category are the same in the case where the calculated average similarity is greater than the predetermined threshold. average ⁢ ⁢ similarity = 1 n × m ⁢ ∑ { ( I 1 ⁢ m · I 2 ⁢ n )  I 1 ⁢ m  ⁢  I 2 ⁢ n  }
5. The object recognition apparatus according to claim 4 , wherein in the case where the determination unit has determined that the first dictionary information included in the first category and the second dictionary information included in the second category are the same and the sum of m and n is greater than a predetermined number N, the integration unit uses the following formula, which finds the sum of similarities of the ith dictionary information, to arrange the dictionary information in ascending order of sum of similarities as calculated for each of the m pieces of first dictionary information and the n pieces of second dictionary information, and deletes the N+1th and following dictionary information from the first category or the second category. ( similarity ⁢ ⁢ sum ) i = ∑ i ≠ j ⁢ { ( I i · I j )  I i  ⁢  I j  } ⁢ [ i = 1 , 2 ⁢ ⁢ … ⁢ ⁢ n × m j = 1 , 2 ⁢ ⁢ … ⁢ ⁢ n × m ]